mbutrovich commented on code in PR #2010: URL: https://github.com/apache/datafusion-comet/pull/2010#discussion_r2210833524
########## native/spark-expr/src/nondetermenistic_funcs/randn.rs: ########## @@ -0,0 +1,265 @@ +// 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. + +use crate::nondetermenistic_funcs::rand::XorShiftRandom; + +use crate::internal::{evaluate_batch_for_rand, StatefulSeedValueGenerator}; +use arrow::array::RecordBatch; +use arrow::datatypes::{DataType, Schema}; +use datafusion::common::DataFusionError; +use datafusion::logical_expr::ColumnarValue; +use datafusion::physical_expr::PhysicalExpr; +use std::any::Any; +use std::fmt::{Display, Formatter}; +use std::hash::{Hash, Hasher}; +use std::sync::{Arc, Mutex}; + +/// Stateful extension of the Marsaglia polar method (https://en.wikipedia.org/wiki/Marsaglia_polar_method) +/// to convert uniform distribution to the standard normal one used by Apache Spark. +/// For correct processing of batches having odd number of elements, we need to keep not used yet generated value as a part of the state. +/// Note about Comet <-> Spark equivalence: +/// Under the hood, the spark algorithm refers to java.util.Random relying on a module StrictMath. The latter uses +/// native implementations of floating-point operations (ln, exp, sin, cos) and ensures +/// they are stable across different platforms. +/// See: https://github.com/openjdk/jdk/blob/07c9f7138affdf0d42ecdc30adcb854515569985/src/java.base/share/classes/java/util/Random.java#L745 +/// Yet, for the Rust standard library this stability is not guaranteed (https://doc.rust-lang.org/std/primitive.f64.html#method.ln) +/// Moreover, potential usage of external library like rug (https://docs.rs/rug/latest/rug/) doesn't help because still there is no +/// guarantee it matches the StrictMath jvm implementation. +/// So, we can ensure only equivalence with some error tolerance between rust and spark(jvm). + +#[derive(Debug, Clone)] +struct XorShiftRandomForGaussian { + base_generator: XorShiftRandom, + next_gaussian: Option<f64>, +} + +impl XorShiftRandomForGaussian { + pub fn next_gaussian(&mut self) -> f64 { + if let Some(stored_value) = self.next_gaussian { + self.next_gaussian = None; + return stored_value; + } + let mut v1: f64; + let mut v2: f64; + let mut s: f64; + loop { + v1 = 2f64 * self.base_generator.next_f64() - 1f64; + v2 = 2f64 * self.base_generator.next_f64() - 1f64; + s = v1 * v1 + v2 * v2; + if s < 1f64 && s != 0f64 { + break; + } + } + let multiplier = (-2f64 * s.ln() / s).sqrt(); + self.next_gaussian = Some(v2 * multiplier); + v1 * multiplier + } +} + +type RandomGaussianState = (i64, Option<f64>); + +impl StatefulSeedValueGenerator<RandomGaussianState, f64> for XorShiftRandomForGaussian { + fn from_init_seed(init_value: i64) -> Self { + XorShiftRandomForGaussian { + base_generator: XorShiftRandom::from_init_seed(init_value), + next_gaussian: None, + } + } + + fn from_stored_state(stored_state: RandomGaussianState) -> Self { + XorShiftRandomForGaussian { + base_generator: XorShiftRandom::from_stored_state(stored_state.0), + next_gaussian: stored_state.1, + } + } + + fn next_value(&mut self) -> f64 { + self.next_gaussian() + } + + fn get_current_state(&self) -> RandomGaussianState { + (self.base_generator.seed, self.next_gaussian) + } +} + +#[derive(Debug, Clone)] +pub struct RandnExpr { + seed: Arc<dyn PhysicalExpr>, + init_seed_shift: i32, + state_holder: Arc<Mutex<Option<RandomGaussianState>>>, +} + +impl RandnExpr { + pub fn new(seed: Arc<dyn PhysicalExpr>, init_seed_shift: i32) -> Self { + Self { + seed, + init_seed_shift, + state_holder: Arc::new(Mutex::new(None)), + } + } +} + +impl Display for RandnExpr { + fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { + write!(f, "RANDN({})", self.seed) + } +} + +impl PartialEq for RandnExpr { + fn eq(&self, other: &Self) -> bool { + self.seed.eq(&other.seed) && self.init_seed_shift == other.init_seed_shift + } +} + +impl Eq for RandnExpr {} + +impl Hash for RandnExpr { + fn hash<H: Hasher>(&self, state: &mut H) { + self.children().hash(state); + } +} + +impl PhysicalExpr for RandnExpr { Review Comment: That would be fantastic, thank you! -- This is an automated message from the Apache Git Service. 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