[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1068598889


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +152,597 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_multiple_inputs(
+batch: pd.DataFrame, input_shapes: List[List[int] | None], num_input_cols: 
int
+) -> List[np.ndarray]:
+multi_inputs = [batch[col].to_numpy() for col in batch.columns]
+if input_shapes:
+if len(input_shapes) == num_input_cols:
+multi_inputs = [
+np.vstack(v).reshape([-1] + input_shapes[i])  # type: ignore
+if input_shapes[i]
+else v
+for i, v in enumerate(multi_inputs)
+]
+if not all([len(x) == len(batch) for x in multi_inputs]):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("input_tensor_shapes must match columns")
+
+return multi_inputs
+
+
+def _validate_and_transform_single_input(
+batch: pd.DataFrame,
+input_shapes: List[List[int] | None],
+has_tensors: bool,
+has_tuple: bool,
+) -> np.ndarray:
+# multiple input columns for single expected input
+if has_tensors:
+# tensor columns
+if len(batch.columns) == 1:
+# one tensor column and one expected input, vstack rows
+single_input = np.vstack(batch.iloc[:, 0])
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+else:
+# scalar columns
+if len(batch.columns) == 1:
+# single scalar column, remove extra dim
+single_input = np.squeeze(batch.to_numpy())
+if input_shapes and input_shapes[0] not in [None, [], [1]]:
+raise ValueError("Invalid input_tensor_shape for scalar 
column.")
+elif not has_tuple:
+# columns grouped via struct/array, convert to single tensor
+single_input = batch.to_numpy()
+if input_shapes and input_shapes[0] != [len(batch.columns)]:
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+
+# if input_tensor_shapes provided, try to reshape input
+if input_shapes:
+if len(input_shapes) == 1:
+single_input = single_input.reshape([-1] + input_shapes[0])  # 
type: ignore
+if len(single_input) != len(batch):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("Multiple input_tensor_shapes found, but model 
expected one input")
+
+return single_input
+
+
+def _validate_and_transform_prediction_result(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1068492718


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +152,597 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_multiple_inputs(
+batch: pd.DataFrame, input_shapes: List[List[int] | None], num_input_cols: 
int
+) -> List[np.ndarray]:
+multi_inputs = [batch[col].to_numpy() for col in batch.columns]
+if input_shapes:
+if len(input_shapes) == num_input_cols:
+multi_inputs = [
+np.vstack(v).reshape([-1] + input_shapes[i])  # type: ignore
+if input_shapes[i]
+else v
+for i, v in enumerate(multi_inputs)
+]
+if not all([len(x) == len(batch) for x in multi_inputs]):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("input_tensor_shapes must match columns")
+
+return multi_inputs
+
+
+def _validate_and_transform_single_input(
+batch: pd.DataFrame,
+input_shapes: List[List[int] | None],
+has_tensors: bool,
+has_tuple: bool,
+) -> np.ndarray:
+# multiple input columns for single expected input
+if has_tensors:
+# tensor columns
+if len(batch.columns) == 1:
+# one tensor column and one expected input, vstack rows
+single_input = np.vstack(batch.iloc[:, 0])
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+else:
+# scalar columns
+if len(batch.columns) == 1:
+# single scalar column, remove extra dim
+single_input = np.squeeze(batch.to_numpy())
+if input_shapes and input_shapes[0] not in [None, [], [1]]:
+raise ValueError("Invalid input_tensor_shape for scalar 
column.")
+elif not has_tuple:
+# columns grouped via struct/array, convert to single tensor
+single_input = batch.to_numpy()
+if input_shapes and input_shapes[0] != [len(batch.columns)]:
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+
+# if input_tensor_shapes provided, try to reshape input
+if input_shapes:
+if len(input_shapes) == 1:
+single_input = single_input.reshape([-1] + input_shapes[0])  # 
type: ignore
+if len(single_input) != len(batch):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("Multiple input_tensor_shapes found, but model 
expected one input")
+
+return single_input
+
+
+def _validate_and_transform_prediction_result(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1061936672


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +138,605 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_multiple_inputs(
+batch: pd.DataFrame, input_shapes: List[List[int] | None], num_input_cols: 
int
+) -> List[np.ndarray]:
+multi_inputs = [batch[col].to_numpy() for col in batch.columns]
+if input_shapes:
+if len(input_shapes) == num_input_cols:
+multi_inputs = [
+np.vstack(v).reshape([-1] + input_shapes[i])  # type: ignore
+if input_shapes[i]
+else v
+for i, v in enumerate(multi_inputs)
+]
+if not all([len(x) == len(batch) for x in multi_inputs]):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("input_tensor_shapes must match columns")
+
+return multi_inputs
+
+
+def _validate_and_transform_single_input(
+batch: pd.DataFrame,
+input_shapes: List[List[int] | None],
+has_tensors: bool,
+has_tuple: bool,
+) -> np.ndarray:
+# multiple input columns for single expected input
+if has_tensors:
+# tensor columns
+if len(batch.columns) == 1:
+# one tensor column and one expected input, vstack rows
+single_input = np.vstack(batch.iloc[:, 0])
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+else:
+# scalar columns
+if len(batch.columns) == 1:
+# single scalar column, remove extra dim
+single_input = np.squeeze(batch.to_numpy())
+if input_shapes and input_shapes[0] not in [None, [], [1]]:
+raise ValueError("Invalid input_tensor_shape for scalar 
column.")
+elif not has_tuple:
+# columns grouped via struct/array, convert to single tensor
+single_input = batch.to_numpy()
+if input_shapes and input_shapes[0] != [len(batch.columns)]:
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+
+# if input_tensor_shapes provided, try to reshape input
+if input_shapes:
+if len(input_shapes) == 1:
+single_input = single_input.reshape([-1] + input_shapes[0])  # 
type: ignore
+if len(single_input) != len(batch):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("Multiple input_tensor_shapes found, but model 
expected one input")
+
+return single_input
+
+
+def _validate_and_transform_prediction_result(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1061926376


##
python/pyspark/ml/model_cache.py:
##
@@ -0,0 +1,44 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from collections import OrderedDict
+from threading import Lock
+from typing import Callable, Optional
+from uuid import UUID
+
+
+class ModelCache:
+"""Cache for model prediction functions on executors."""
+
+_models: OrderedDict[UUID, Callable] = OrderedDict()
+_capacity: int = 8

Review Comment:
   Changed default capacity to 3 and updated docs.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1061926045


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |

Review Comment:
   Updated docs per comments.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1061925652


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],

Review Comment:
   Added a new `PredictFunction` type to account for varargs of ndarray.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1019742581


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,602 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_multiple_inputs(
+batch: pd.DataFrame, input_shapes: List[List[int] | None], num_input_cols: 
int
+) -> List[np.ndarray]:
+multi_inputs = [batch[col].to_numpy() for col in batch.columns]
+if input_shapes:
+if len(input_shapes) == num_input_cols:
+multi_inputs = [
+np.vstack(v).reshape([-1] + input_shapes[i])  # type: ignore
+if input_shapes[i]
+else v
+for i, v in enumerate(multi_inputs)
+]
+if not all([len(x) == len(batch) for x in multi_inputs]):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("input_tensor_shapes must match columns")
+
+return multi_inputs
+
+
+def _validate_and_transform_single_input(
+batch: pd.DataFrame,
+input_shapes: List[List[int] | None],
+has_tensors: bool,
+has_tuple: bool,
+) -> np.ndarray:
+# multiple input columns for single expected input
+if has_tensors:
+# tensor columns
+if len(batch.columns) == 1:
+# one tensor column and one expected input, vstack rows
+single_input = np.vstack(batch.iloc[:, 0])
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+else:
+# scalar columns
+if len(batch.columns) == 1:
+# single scalar column, remove extra dim
+single_input = np.squeeze(batch.to_numpy())
+if input_shapes and input_shapes[0] not in [None, [], [1]]:
+raise ValueError("Invalid input_tensor_shape for scalar 
column.")
+elif not has_tuple:
+# columns grouped via struct/array, convert to single tensor
+single_input = batch.to_numpy()
+if input_shapes and input_shapes[0] != [len(batch.columns)]:
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+
+# if input_tensor_shapes provided, try to reshape input
+if input_shapes:
+if len(input_shapes) == 1:
+single_input = single_input.reshape([-1] + input_shapes[0])  # 
type: ignore
+if len(single_input) != len(batch):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("Multiple input_tensor_shapes found, but model 
expected one input")
+
+return single_input
+
+
+def _validate_and_transform_prediction_result(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1019742581


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,602 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_multiple_inputs(
+batch: pd.DataFrame, input_shapes: List[List[int] | None], num_input_cols: 
int
+) -> List[np.ndarray]:
+multi_inputs = [batch[col].to_numpy() for col in batch.columns]
+if input_shapes:
+if len(input_shapes) == num_input_cols:
+multi_inputs = [
+np.vstack(v).reshape([-1] + input_shapes[i])  # type: ignore
+if input_shapes[i]
+else v
+for i, v in enumerate(multi_inputs)
+]
+if not all([len(x) == len(batch) for x in multi_inputs]):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("input_tensor_shapes must match columns")
+
+return multi_inputs
+
+
+def _validate_and_transform_single_input(
+batch: pd.DataFrame,
+input_shapes: List[List[int] | None],
+has_tensors: bool,
+has_tuple: bool,
+) -> np.ndarray:
+# multiple input columns for single expected input
+if has_tensors:
+# tensor columns
+if len(batch.columns) == 1:
+# one tensor column and one expected input, vstack rows
+single_input = np.vstack(batch.iloc[:, 0])
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+else:
+# scalar columns
+if len(batch.columns) == 1:
+# single scalar column, remove extra dim
+single_input = np.squeeze(batch.to_numpy())
+if input_shapes and input_shapes[0] not in [None, [], [1]]:
+raise ValueError("Invalid input_tensor_shape for scalar 
column.")
+elif not has_tuple:
+# columns grouped via struct/array, convert to single tensor
+single_input = batch.to_numpy()
+if input_shapes and input_shapes[0] != [len(batch.columns)]:
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError(
+"Multiple input columns found, but model expected a single "
+"input, use `struct` or `array` to combine columns into 
tensors."
+)
+
+# if input_tensor_shapes provided, try to reshape input
+if input_shapes:
+if len(input_shapes) == 1:
+single_input = single_input.reshape([-1] + input_shapes[0])  # 
type: ignore
+if len(single_input) != len(batch):
+raise ValueError("Input data does not match expected shape.")
+else:
+raise ValueError("Multiple input_tensor_shapes found, but model 
expected one input")
+
+return single_input
+
+
+def _validate_and_transform_prediction_result(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1018209884


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,556 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform_prediction_result(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+if isinstance(return_type, StructType):
+struct_rtype: StructType = return_type
+fieldNames = struct_rtype.names
+if isinstance(preds, dict):
+# dictionary of columns
+predNames = list(preds.keys())
+for field in struct_rtype.fields:
+if isinstance(field.dataType, ArrayType):
+if len(preds[field.name].shape) == 2:
+preds[field.name] = list(preds[field.name])
+else:
+raise ValueError(
+"Prediction results for ArrayType must be 
two-dimensional."
+)
+if len(preds[field.name]) != num_input_rows:
+raise ValueError("Prediction results must have same length 
as input data")
+
+elif isinstance(preds, list) and isinstance(preds[0], dict):
+# rows of dictionaries
+predNames = list(preds[0].keys())
+if len(preds) != num_input_rows:
+raise ValueError("Prediction results must have same length as 
input data.")
+else:
+raise ValueError(
+"Prediction results for StructType must be a dictionary or "
+"a list of dictionary, got: {}".format(type(preds))
+)
+
+# check column names
+if set(predNames) != set(fieldNames):
+raise ValueError(
+"Prediction result columns did not match expected return_type "
+"columns: expected {}, got: {}".format(fieldNames, predNames)
+)
+
+return pd.DataFrame(preds)
+elif isinstance(return_type, ArrayType):
+if isinstance(preds, np.ndarray):
+if len(preds) != num_input_rows:
+raise ValueError("Prediction results must have same length as 
input data.")
+if len(preds.shape) != 2:
+raise ValueError("Prediction results for ArrayType must be 
two-dimensional.")
+else:
+raise ValueError("Prediction results for ArrayType must be an 
ndarray.")
+
+return pd.Series(list(preds))
+else:  # scalar
+if len(preds) != num_input_rows:
+raise ValueError("Prediction results must have same length as 
input data.")
+
+return pd.Series(np.squeeze(preds))  # type: ignore
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: List[List[int] | None] | Mapping[int, List[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given 

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1015893038


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,543 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+df = data
+elif isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate_and_transform(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> pd.DataFrame | pd.Series:
+"""Validate numpy-based model predictions against the expected pandas_udf 
return_type and
+transforms the predictions into an equivalent pandas DataFrame or 
Series."""
+if isinstance(return_type, StructType):
+struct_rtype: StructType = return_type
+fieldNames = struct_rtype.names
+if isinstance(preds, dict):
+# dictionary of columns
+predNames = list(preds.keys())
+for field in struct_rtype.fields:
+if len(preds[field.name]) != num_input_rows:
+raise ValueError("Prediction results must have same length 
as input data.")
+if field.dataType == ArrayType and preds[field.name].shape != 
2:

Review Comment:
   Good catch.  Fixed and added test case.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1015892376


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,542 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+index = 0
+data_size = len(data)
+while index < data_size:
+yield data.iloc[index : index + batch_size]
+index += batch_size
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> None:
+"""Validate model predictions against the expected pandas_udf 
return_type."""
+if isinstance(return_type, StructType):
+struct_rtype: StructType = return_type
+fieldNames = struct_rtype.names
+if isinstance(preds, dict):
+# dictionary of columns
+predNames = list(preds.keys())
+if not all(v.shape == (num_input_rows,) for v in preds.values()):
+raise ValueError("Prediction results for StructType fields 
must be scalars.")
+elif isinstance(preds, list) and isinstance(preds[0], dict):
+# rows of dictionaries
+predNames = list(preds[0].keys())
+if len(preds) != num_input_rows:
+raise ValueError("Prediction results must have same length as 
input data.")

Review Comment:
   Added to pydoc per comment below.



##
python/pyspark/ml/functions.py:
##
@@ -162,20 +156,25 @@ def _has_tensor_cols(data: pd.Series | pd.DataFrame | 
Tuple[pd.Series]) -> bool:
 return any(_is_tensor_col(elem) for elem in data)
 
 
-def _validate(
+def _validate_and_transform(

Review Comment:
   Sure



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1014476080


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,542 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+index = 0
+data_size = len(data)
+while index < data_size:
+yield data.iloc[index : index + batch_size]
+index += batch_size
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+
+index = 0
+data_size = len(df)
+while index < data_size:
+yield df.iloc[index : index + batch_size]
+index += batch_size
+
+
+def _is_tensor_col(data: pd.Series | pd.DataFrame) -> bool:
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:
+raise ValueError(
+"Unexpected data type: {}, expected pd.Series or 
pd.DataFrame.".format(type(data))
+)
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input Series/DataFrame/Tuple contains any tensor-valued 
columns."""
+if isinstance(data, (pd.Series, pd.DataFrame)):
+return _is_tensor_col(data)
+else:  # isinstance(data, Tuple):
+return any(_is_tensor_col(elem) for elem in data)
+
+
+def _validate(
+preds: np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, Any]],
+num_input_rows: int,
+return_type: DataType,
+) -> None:
+"""Validate model predictions against the expected pandas_udf 
return_type."""
+if isinstance(return_type, StructType):
+struct_rtype: StructType = return_type
+fieldNames = struct_rtype.names
+if isinstance(preds, dict):
+# dictionary of columns
+predNames = list(preds.keys())
+if not all(v.shape == (num_input_rows,) for v in preds.values()):
+raise ValueError("Prediction results for StructType fields 
must be scalars.")
+elif isinstance(preds, list) and isinstance(preds[0], dict):
+# rows of dictionaries
+predNames = list(preds[0].keys())
+if len(preds) != num_input_rows:
+raise ValueError("Prediction results must have same length as 
input data.")

Review Comment:
   Yeah, @mengxr had the same question.  Some models, e.g. [Huggingface 
pipeline for sentiment 
analysis](https://huggingface.co/docs/transformers/quicktour#pipeline-usage), 
can produce results in this format, so he agreed to keep this case.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1014475066


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,542 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+index = 0
+data_size = len(data)
+while index < data_size:
+yield data.iloc[index : index + batch_size]
+index += batch_size

Review Comment:
   Ended up refactoring the code a bit to simplify this.



-- 
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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1012129510


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010991074


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010991074


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010663824


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010663824


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010681773


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010660127


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010670125


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010663824


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r1010660127


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +117,474 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def _batched(
+data: pd.Series | pd.DataFrame | Tuple[pd.Series], batch_size: int
+) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if isinstance(data, pd.DataFrame):
+for _, batch in data.groupby(np.arange(len(data)) // batch_size):
+yield batch
+else:
+# convert (tuple of) pd.Series into pd.DataFrame
+if isinstance(data, pd.Series):
+df = pd.concat((data,), axis=1)
+else:  # isinstance(data, Tuple[pd.Series]):
+df = pd.concat(data, axis=1)
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def _has_tensor_cols(data: pd.Series | pd.DataFrame | Tuple[pd.Series]) -> 
bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if isinstance(data, pd.Series):
+return data.dtype == np.object_ and isinstance(data.iloc[0], 
(np.ndarray, list))
+elif isinstance(data, pd.DataFrame):
+return any(data.dtypes == np.object_) and any(
+[isinstance(d, (np.ndarray, list)) for d in data.iloc[0]]
+)
+else:  # isinstance(data, Tuple):
+return any([d.dtype == np.object_ for d in data]) and any(
+[isinstance(d.iloc[0], (np.ndarray, list)) for d in data]
+)
+
+
+def predict_batch_udf(
+predict_batch_fn: Callable[
+[],
+Callable[
+[np.ndarray | List[np.ndarray]],
+np.ndarray | Mapping[str, np.ndarray] | List[Mapping[str, 
np.dtype]],
+],
+],
+*,
+return_type: DataType,
+batch_size: int,
+input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] | 
None = None,
+) -> UserDefinedFunctionLike:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. user must use `pyspark.sql.functions.struct()` or 
`pyspark.sql.functions.array()` to
+   combine multiple input columns into the equivalent of a single-col 
tensor.
+4. pass thru dataframe column => model input as an ordered list of numpy 
arrays.
+
+Example (single-col tensor):
+
+Input DataFrame has a single column with a flattened tensor value, 
represented as an array of
+float.
+```
+from pyspark.ml.functions import predict_batch_udf
+
+def predict_batch_fn():
+# load/init happens once per python worker
+import tensorflow as tf
+model = tf.keras.models.load_model('/path/to/mnist_model')
+
+# predict on batches of tasks/partitions, using cached model
+def predict(inputs: np.ndarray) -> np.ndarray:
+# inputs.shape = [batch_size, 784]
+# outputs.shape = [batch_size, 10], return_type = 
ArrayType(FloatType())
+return model.predict(inputs)
+
+return predict
+
+mnist = predict_batch_udf(predict_batch_fn,
+  return_type=ArrayType(FloatType()),
+  batch_size=100,
+  input_tensor_shapes=[[784]])
+
+df = spark.read.parquet("/path/to/mnist_data")
+df.show(5)
+# ++
+# |data|
+# ++
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# |[0.0, 0.0, 0.0, 0...|
+# ++
+
+df.withColumn("preds", mnist("data")).show(5)
+# +++

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r976953978


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +112,170 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],
+input_tensor_shapes: list[list[int]] = [],
+**kwargs: Any,
+) -> Callable:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+When selecting columns in pyspark SQL, users are required to always use 
`struct` for simplicity.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. convert entire dataframe into single numpy array via df.to_numpy(), or 
user can use
+   `pyspark.sql.functions.array()` to transform the input into a 
single-col tensor first.
+4. pass thru dataframe column => model input as an (ordered) dictionary of 
numpy arrays.
+
+Parameters
+--
+predict_batch_fn : Callable
+Function which is responsible for loading a model and returning a 
`predict` function.
+return_type : DataType
+Spark SQL datatype for the expected output.
+Default: ArrayType(FloatType())
+batch_size : int
+Batch size to use for inference, note that this is typically a 
limitation of the model
+and/or the hardware resources and is usually smaller than the Spark 
partition size.
+Default: -1, which sends the entire Spark partition to the model.
+input_names: list[str]
+Optional list of input names which will be used to map DataFrame 
column names to model
+input names.  The order of names must match the order of the selected 
DataFrame columns.
+If provided, the `predict()` function will be passed a dictionary of 
named inputs.
+input_tensor_shapes: list[list[int]]
+Optional list of input tensor shapes for models with tensor inputs.  
Each tensor
+input must be represented as a single DataFrame column containing a 
flattened 1-D array.
+The order of the tensor shapes must match the order of the selected 
DataFrame columns.
+Tabular datasets with scalar-valued columns should not supply this 
argument.
+
+Returns
+---
+A pandas_udf for predicting a batch.
+"""
+# generate a new uuid each time this is invoked on the driver to 
invalidate executor-side cache.
+model_uuid = uuid.uuid4()
+
+def predict(data: Iterator[pd.DataFrame]) -> Iterator[pd.DataFrame]:
+import pyspark.ml.executor_globals as exec_global
+
+if exec_global.predict_fn and exec_global.model_uuid == model_uuid:
+predict_fn = exec_global.predict_fn
+else:
+predict_fn = predict_batch_fn(**kwargs)
+exec_global.predict_fn = 

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r976741664


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +111,167 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],

Review Comment:
   Per offline discussion with @mengxr, removed `input_names` and 
dictionary-style inputs in favor of list-style inputs.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r974649030


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +111,167 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],

Review Comment:
   - mutable `[]` is fixed.
   - for now, the `input_names` is an explicit signal to the code that the user 
is expecting multiple named inputs, and for most simple (single-input) cases, 
this wouldn't be used/required.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r974646917


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +111,167 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],
+input_tensor_shapes: list[list[int]] = [],
+**kwargs: Any,
+) -> Callable:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+When selecting columns in pyspark SQL, users are required to always use 
`struct` for simplicity.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |

Review Comment:
   I think it's fine to leave this for now... Otherwise, we would just add an 
error response in this case to tell folks to use `array`.  So it seems it'd be 
better to just support it vs. raising an error (unless this causes issues 
elsewhere).



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r974626628


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +111,167 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],
+input_tensor_shapes: list[list[int]] = [],
+**kwargs: Any,
+) -> Callable:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+When selecting columns in pyspark SQL, users are required to always use 
`struct` for simplicity.

Review Comment:
   Updated the code to support `col` and `str` column selections, so `struct` 
is not a requirement any more.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r970164323


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +111,167 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],
+input_tensor_shapes: list[list[int]] = [],
+**kwargs: Any,
+) -> Callable:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+When selecting columns in pyspark SQL, users are required to always use 
`struct` for simplicity.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. convert entire dataframe into single numpy array via df.to_numpy(), or 
user can use
+   `pyspark.sql.functions.array()` to transform the input into a 
single-col tensor first.
+4. pass thru dataframe column => model input as an (ordered) dictionary of 
numpy arrays.
+
+Parameters
+--
+predict_batch_fn : Callable
+Function which is responsible for loading a model and returning a 
`predict` function.
+return_type : :class:`pspark.sql.types.DataType` or str.
+Spark SQL datatype for the expected output.
+Default: ArrayType(FloatType())
+batch_size : int
+Batch size to use for inference, note that this is typically a 
limitation of the model
+and/or the hardware resources and is usually smaller than the Spark 
partition size.
+Default: -1, which sends the entire Spark partition to the model.

Review Comment:
   Ended up just making it a required param for now.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r960972099


##
python/pyspark/ml/executor_globals.py:
##
@@ -0,0 +1,24 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Module to hold globals for python processes on executors
+from typing import Callable, Optional
+from uuid import UUID
+
+
+model_uuid: Optional[UUID] = None
+predict_fn: Optional[Callable] = None

Review Comment:
   Right, updated the code.  ~~Probably still need to figure out a scheme for 
cache invalidation for large models and/or frequent use.~~ Update: looks like 
the python idle worker cleanup is probably sufficient here.



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[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r960986473


##
python/pyspark/ml/functions.py:
##
@@ -106,6 +112,170 @@ def array_to_vector(col: Column) -> Column:
 return 
Column(sc._jvm.org.apache.spark.ml.functions.array_to_vector(_to_java_column(col)))
 
 
+def batched(df: pd.DataFrame, batch_size: int = -1) -> Iterator[pd.DataFrame]:
+"""Generator that splits a pandas dataframe/series into batches."""
+if batch_size <= 0 or batch_size >= len(df):
+yield df
+else:
+# for batch in np.array_split(df, (len(df.index) + batch_size - 1) // 
batch_size):
+for _, batch in df.groupby(np.arange(len(df)) // batch_size):
+yield batch
+
+
+def has_tensor_cols(df: pd.DataFrame) -> bool:
+"""Check if input DataFrame contains any tensor-valued columns"""
+if any(df.dtypes == np.object_):
+# pd.DataFrame object types can contain different types, e.g. string, 
dates, etc.
+# so inspect a row and check for array/list type
+sample = df.iloc[0]
+return any([isinstance(x, np.ndarray) or isinstance(x, list) for x in 
sample])
+else:
+return False
+
+
+def batch_infer_udf(
+predict_batch_fn: Callable,
+return_type: DataType = ArrayType(FloatType()),
+batch_size: int = -1,
+input_names: list[str] = [],
+input_tensor_shapes: list[list[int]] = [],
+**kwargs: Any,
+) -> Callable:
+"""Given a function which loads a model, returns a pandas_udf for 
inferencing over that model.
+
+This will handle:
+- conversion of the Spark DataFrame to numpy arrays.
+- batching of the inputs sent to the model predict() function.
+- caching of the model and prediction function on the executors.
+
+This assumes that the `predict_batch_fn` encapsulates all of the necessary 
dependencies for
+running the model or the Spark executor environment already satisfies all 
runtime requirements.
+
+When selecting columns in pyspark SQL, users are required to always use 
`struct` for simplicity.
+
+For the conversion of Spark DataFrame to numpy, the following table 
describes the behavior,
+where tensor columns in the Spark DataFrame must be represented as a 
flattened 1-D array/list.
+
+| dataframe \\ model | single input | multiple inputs |
+| :- | :--- | :-- |
+| single-col scalar  | 1| N/A |
+| single-col tensor  | 1,2  | N/A |
+| multi-col scalar   | 3| 4   |
+| multi-col tensor   | N/A  | 4,2 |
+
+
+Notes:
+1. pass thru dataframe column => model input as single numpy array.
+2. reshape flattened tensors into expected tensor shapes.
+3. convert entire dataframe into single numpy array via df.to_numpy(), or 
user can use
+   `pyspark.sql.functions.array()` to transform the input into a 
single-col tensor first.
+4. pass thru dataframe column => model input as an (ordered) dictionary of 
numpy arrays.
+
+Parameters
+--
+predict_batch_fn : Callable
+Function which is responsible for loading a model and returning a 
`predict` function.
+return_type : DataType
+Spark SQL datatype for the expected output.
+Default: ArrayType(FloatType())
+batch_size : int
+Batch size to use for inference, note that this is typically a 
limitation of the model
+and/or the hardware resources and is usually smaller than the Spark 
partition size.
+Default: -1, which sends the entire Spark partition to the model.
+input_names: list[str]
+Optional list of input names which will be used to map DataFrame 
column names to model
+input names.  The order of names must match the order of the selected 
DataFrame columns.
+If provided, the `predict()` function will be passed a dictionary of 
named inputs.
+input_tensor_shapes: list[list[int]]
+Optional list of input tensor shapes for models with tensor inputs.  
Each tensor
+input must be represented as a single DataFrame column containing a 
flattened 1-D array.
+The order of the tensor shapes must match the order of the selected 
DataFrame columns.
+Tabular datasets with scalar-valued columns should not supply this 
argument.
+
+Returns
+---
+A pandas_udf for predicting a batch.
+"""
+# generate a new uuid each time this is invoked on the driver to 
invalidate executor-side cache.
+model_uuid = uuid.uuid4()
+
+def predict(data: Iterator[pd.DataFrame]) -> Iterator[pd.DataFrame]:
+import pyspark.ml.executor_globals as exec_global
+
+if exec_global.predict_fn and exec_global.model_uuid == model_uuid:
+predict_fn = exec_global.predict_fn
+else:
+predict_fn = predict_batch_fn(**kwargs)
+exec_global.predict_fn = 

[GitHub] [spark] leewyang commented on a diff in pull request #37734: [SPARK-40264][ML] add batch_infer_udf function to pyspark.ml.functions

[GitBox]

leewyang commented on code in PR #37734:
URL: https://github.com/apache/spark/pull/37734#discussion_r960972099


##
python/pyspark/ml/executor_globals.py:
##
@@ -0,0 +1,24 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Module to hold globals for python processes on executors
+from typing import Callable, Optional
+from uuid import UUID
+
+
+model_uuid: Optional[UUID] = None
+predict_fn: Optional[Callable] = None

Review Comment:
   Right, updated the code.  Probably still need to figure out a scheme for 
cache invalidation for large models and/or frequent use.



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