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)
+ # +--------------------+--------------------+
+ # | data| preds|
+ # +--------------------+--------------------+
+ # |[0.0, 0.0, 0.0, 0...|[-13.511008, 8.84...|
+ # |[0.0, 0.0, 0.0, 0...|[-5.3957458, -2.2...|
+ # |[0.0, 0.0, 0.0, 0...|[-7.2014456, -8.8...|
+ # |[0.0, 0.0, 0.0, 0...|[-19.466187, -13....|
+ # |[0.0, 0.0, 0.0, 0...|[-5.7757926, -7.8...|
+ # +--------------------+--------------------+
+ ```
+
+ Example (single-col scalar):
+
+ Input DataFrame has a single scalar column, which will be passed to the
`predict` function as
+ a 1-D numpy array.
+ ```
+ import numpy as np
+ import pandas as pd
+ from pyspark.ml.functions import predict_batch_udf
+ from pyspark.sql.types import FloatType
+
+ df = spark.createDataFrame(pd.DataFrame(np.arange(100)))
+ df.show(5)
+ # +---+
+ # | 0|
+ # +---+
+ # | 0|
+ # | 1|
+ # | 2|
+ # | 3|
+ # | 4|
+ # +---+
+
+ def predict_batch_fn():
+ def predict(inputs: np.ndarray) -> np.ndarray:
+ # inputs.shape = [batch_size]
+ # outputs.shape = [batch_size], return_type = FloatType()
+ return inputs * 2
+
+ return predict
+
+ times_two = predict_batch_udf(predict_batch_fn,
+ return_type=FloatType(),
+ batch_size=10)
+
+ df = spark.createDataFrame(pd.DataFrame(np.arange(100)))
+ df.withColumn("x2", times_two("0")).show(5)
+ # +---+---+
+ # | 0| x2|
+ # +---+---+
+ # | 0|0.0|
+ # | 1|2.0|
+ # | 2|4.0|
+ # | 3|6.0|
+ # | 4|8.0|
+ # +---+---+
+ ```
+
+ Example (multi-col scalar):
+
+ Input DataFrame has muliple columns of scalar values. If the
user-provided `predict` function
+ expects a single input, then the user should combine multiple columns into
a single tensor using
+ `pyspark.sql.functions.struct` or `pyspark.sql.functions.array`.
+ ```
+ import numpy as np
+ import pandas as pd
+ from pyspark.ml.functions import predict_batch_udf
+ from pyspark.sql.functions import struct
+
+ data = np.arange(0, 1000, dtype=np.float64).reshape(-1, 4)
+ pdf = pd.DataFrame(data, columns=['a','b','c','d'])
+ df = spark.createDataFrame(pdf)
+ # +----+----+----+----+
+ # | a| b| c| d|
+ # +----+----+----+----+
+ # | 0.0| 1.0| 2.0| 3.0|
+ # | 4.0| 5.0| 6.0| 7.0|
+ # | 8.0| 9.0|10.0|11.0|
+ # |12.0|13.0|14.0|15.0|
+ # |16.0|17.0|18.0|19.0|
+ # +----+----+----+----+
+
+ def predict_batch_fn():
+ def predict(inputs: np.ndarray) -> np.ndarray:
+ # inputs.shape = [batch_size, 4]
+ # outputs.shape = [batch_size], return_type = FloatType()
+ return np.sum(inputs, axis=1)
+
+ return predict
+
+ sum_rows = predict_batch_udf(predict_batch_fn,
+ return_type=FloatType(),
+ batch_size=10,
+ input_tensor_shapes=[[4]])
+
+ df.withColumn("sum", sum_rows(struct("a", "b", "c", "d"))).show(5)
+ # +----+----+----+----+----+
+ # | a| b| c| d| sum|
+ # +----+----+----+----+----+
+ # | 0.0| 1.0| 2.0| 3.0| 6.0|
+ # | 4.0| 5.0| 6.0| 7.0|22.0|
+ # | 8.0| 9.0|10.0|11.0|38.0|
+ # |12.0|13.0|14.0|15.0|54.0|
+ # |16.0|17.0|18.0|19.0|70.0|
+ # +----+----+----+----+----+
+
+ # Note: if the `predict` function expects multiple inputs, then the number
of selected columns
+ # must match the number of expected inputs.
+
+ def predict_batch_fn():
+ def predict(x1: np.ndarray, x2: np.ndarray, x3: np.ndarray, x4:
np.ndarray) -> np.ndarray:
+ # xN.shape = [batch_size]
+ # outputs.shape = [batch_size], return_type = FloatType()
+ return x1 + x2 + x3 + x4
+
+ return predict
+
+ sum_rows = predict_batch_udf(predict_batch_fn,
+ return_type=FloatType(),
+ batch_size=10)
+
+ df.withColumn("sum", sum_rows("a", "b", "c", "d")).show(5)
+ # +----+----+----+----+----+
+ # | a| b| c| d| sum|
+ # +----+----+----+----+----+
+ # | 0.0| 1.0| 2.0| 3.0| 6.0|
+ # | 4.0| 5.0| 6.0| 7.0|22.0|
+ # | 8.0| 9.0|10.0|11.0|38.0|
+ # |12.0|13.0|14.0|15.0|54.0|
+ # |16.0|17.0|18.0|19.0|70.0|
+ # +----+----+----+----+----+
+ ```
+
+ Example (multi-col tensor):
+
+ Input DataFrame has multiple columns, where each column is a tensor. The
number of columns
+ should match the number of expected inputs for the user-provided `predict`
function.
+ ```
+ import numpy as np
+ import pandas as pd
+ from pyspark.ml.functions import predict_batch_udf
+ from pyspark.sql.types import FloatType, StructType, StructField
+ from typing import Mapping
+
+ data = np.arange(0, 1000, dtype=np.float64).reshape(-1, 4)
+ pdf = pd.DataFrame(data, columns=['a','b','c','d'])
+ pdf_tensor = pd.DataFrame()
+ pdf_tensor['t1'] = pdf.values.tolist()
+ pdf_tensor['t2'] = pdf.drop(columns='d').values.tolist()
+ df = spark.createDataFrame(pdf_tensor)
+ df.show(5)
+ # +--------------------+------------------+
+ # | t1| t2|
+ # +--------------------+------------------+
+ # |[0.0, 1.0, 2.0, 3.0]| [0.0, 1.0, 2.0]|
+ # |[4.0, 5.0, 6.0, 7.0]| [4.0, 5.0, 6.0]|
+ # |[8.0, 9.0, 10.0, ...| [8.0, 9.0, 10.0]|
+ # |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]|
+ # |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|
+ # +--------------------+------------------+
+
+ def multi_sum_fn():
+ def predict(x1: np.ndarray, x2: np.ndarray) -> np.ndarray:
+ # x1.shape = [batch_size, 4]
+ # x2.shape = [batch_size, 3]
+ # outputs.shape = [batch_size], result_type = FloatType()
+ return np.sum(x1, axis=1) + np.sum(x2, axis=1)
+
+ return predict
+
+ sum_cols = predict_batch_udf(
+ multi_sum_fn,
+ return_type=FloatType(),
+ batch_size=5,
+ input_tensor_shapes=[[4], [3]],
+ )
+
+ df.withColumn("sum", sum_cols("t1", "t2")).show(5)
+ # +--------------------+------------------+-----+
+ # | t1| t2| sum|
+ # +--------------------+------------------+-----+
+ # |[0.0, 1.0, 2.0, 3.0]| [0.0, 1.0, 2.0]| 9.0|
+ # |[4.0, 5.0, 6.0, 7.0]| [4.0, 5.0, 6.0]| 37.0|
+ # |[8.0, 9.0, 10.0, ...| [8.0, 9.0, 10.0]| 65.0|
+ # |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]| 93.0|
+ # |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|121.0|
+ # +--------------------+------------------+-----+
+
+ # Note that some models can provide multiple outputs. These can be
returned as a dictionary
+ # of named values, which can be represented in columnar (or row-based)
formats.
+
+ def multi_sum_fn():
+ def predict_columnar(x1: np.ndarray, x2: np.ndarray) -> Mapping[str,
np.ndarray]:
+ # x1.shape = [batch_size, 4]
+ # x2.shape = [batch_size, 3]
+ return {
+ "sum1": np.sum(x1, axis=1),
+ "sum2": np.sum(x2, axis=1)
+ } # return_type = StructType()
+
+ return predict_columnar
+
+ sum_cols = predict_batch_udf(
+ multi_sum_fn,
+ return_type=StructType([
+ StructField("sum1", FloatType(), True),
+ StructField("sum2", FloatType(), True)
+ ])
+ batch_size=5,
+ input_tensor_shapes=[[4], [3]],
+ )
+
+ df.withColumn("preds", sum_cols("t1", "t2")).select("t1", "t2",
"preds.*").show(5)
+ # +--------------------+------------------+----+----+
+ # | t1| t2|sum1|sum2|
+ # +--------------------+------------------+----+----+
+ # |[0.0, 1.0, 2.0, 3.0]| [0.0, 1.0, 2.0]| 6.0| 3.0|
+ # |[4.0, 5.0, 6.0, 7.0]| [4.0, 5.0, 6.0]|22.0|15.0|
+ # |[8.0, 9.0, 10.0, ...| [8.0, 9.0, 10.0]|38.0|27.0|
+ # |[12.0, 13.0, 14.0...|[12.0, 13.0, 14.0]|54.0|39.0|
+ # |[16.0, 17.0, 18.0...|[16.0, 17.0, 18.0]|70.0|51.0|
+ # +--------------------+------------------+----+----+
+ ```
+
+ Parameters
+ ----------
+ predict_batch_fn : Callable[[],
+ Callable[..., np.ndarray | Mapping[str, np.ndarray] |
List[Mapping[str, np.dtype]] ]
+ Function which is responsible for loading a model and returning a
`predict` function which
+ takes one or more numpy arrays as input and returns either a numpy
array (for a single
+ output), a dictionary of named numpy arrays (for multiple outputs), or
a row-oriented list
+ of dictionaries (for multiple outputs).
+ return_type : :class:`pspark.sql.types.DataType` or str.
+ Spark SQL datatype for the expected output.
+ 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.
+ input_tensor_shapes: list[list[int] | None] | Mapping[int, list[int]] |
None
+ Optional input tensor shapes for models with tensor inputs. This can
be a list of shapes,
+ where each shape is a list of integers or None (for scalar inputs).
Alternatively, this
+ can be represented by a "sparse" dictionary, where the keys are the
integer indices of the
+ inputs, and the values are the shapes. Each tensor input value in the
Spark DataFrame must
+ be represented as a single column containing a flattened 1-D array.
The provided
+ input_tensor_shapes will be used to reshape the flattened array into
expected tensor shape.
+ For the list form, the order of the tensor shapes must match the order
of the selected
+ DataFrame columns. The batch dimension (typically -1 or None in the
first dimension) should
+ not be included, since it will be determined by the batch_size
argument. Tabular datasets
+ with scalar-valued columns should not provide 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[Union[pd.Series, pd.DataFrame]]) ->
Iterator[pd.DataFrame]:
+ from pyspark.ml.model_cache import ModelCache
+
+ # get predict function (from cache or from running user-provided
predict_batch_fn)
+ predict_fn = ModelCache.get(model_uuid)
Review Comment:
Yes, this is expected. The code is modeled after MLFlow's SparkModelCache
(per the [prior
comments](https://github.com/apache/spark/pull/37734#discussion_r960529530)),
which has the same behavior/issues, AFAICT.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
