WeichenXu123 commented on code in PR #37734: URL: https://github.com/apache/spark/pull/37734#discussion_r1016156186
########## 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 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 through 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| + # +--------------------+------------------+----+----+ + ``` + + .. versionadded:: 3.4.0 + + 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 Review Comment: ditto -- This is an automated message from the Apache Git Service. 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