From 552978680f962fa78ecb96e0ef738ae43859977a Mon Sep 17 00:00:00 2001 From: Rowan Cockett Date: Tue, 29 Oct 2024 23:00:01 -0600 Subject: [PATCH] updates --- papers/aleksandar_makelov/full_text.pdf | Bin 0 -> 562296 bytes papers/aleksandar_makelov/main.tex | 196 +++++++++++++++--------- papers/aleksandar_makelov/meca.zip | Bin 0 -> 201765 bytes papers/cockett_etal/full_text.pdf | Bin 6897583 -> 6897583 bytes papers/cockett_etal/meca.zip | Bin 11942180 -> 11941365 bytes papers/matt_mccormick/full_text.pdf | Bin 6276637 -> 6276637 bytes papers/matt_mccormick/meca.zip | Bin 7712924 -> 7712310 bytes papers/matthew_feickert/full_text.pdf | Bin 788715 -> 788715 bytes papers/matthew_feickert/meca.zip | Bin 607310 -> 607310 bytes papers/sam_morley/full_text.pdf | Bin 193382 -> 193382 bytes papers/sam_morley/meca.zip | Bin 44154 -> 43605 bytes 11 files changed, 127 insertions(+), 69 deletions(-) create mode 100644 papers/aleksandar_makelov/full_text.pdf create mode 100644 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zPpij)EdV86UEB|F-zJ@$0vJe*Nm~w2Yj0qHxdze}(v$=Ipyee7tZ``j3ubuwa=;d# zl`Ei3==cGY6CFRmsDuu(LjQiP&oN*%NH=CMP%?Dda)57i{RJw5b{qkp676~)O#1ZW z2v}G2bV1?i`2q5X(BT5sE_Aq{QMB_Qpgp}@VZfT0j()(Zp_LaP2(-2ZSoNS5+Wf!? z3Xb(qSgFUC&IVE@8>P+P08_N&eNQ cgcS9(3fQ}#cu;+MU}*tX4XongT06A=4=y(b#Q*>R literal 0 HcmV?d00001 diff --git a/papers/aleksandar_makelov/main.tex b/papers/aleksandar_makelov/main.tex index 9872dbc4b3..6bcb099ae8 100644 --- a/papers/aleksandar_makelov/main.tex +++ b/papers/aleksandar_makelov/main.tex @@ -1,3 +1,5 @@ +\title[Mandala]{Mandala: Compositional Memoization for Simple & Powerful Scientific Data Management} + \begin{abstract} We present \href{https://github.com/amakelov/mandala}{\texttt{mandala}}, a Python @@ -56,17 +58,56 @@ \section{Introduction} \centering \begin{subfigure}{0.23\textwidth} \centering -\includegraphics[width=\textwidth]{img/fig1.pdf} +% \includegraphics[width=0.23\textwidth]{img/fig1.pdf} +\begin{lstlisting}[language=python] +# decorate any +# Python funcs +@op +def f(x): + return x**2 + +@op +def g(x, y): + return x + y + +... +\end{lstlisting} \caption{} \end{subfigure} \begin{subfigure}{0.35\textwidth} \centering -\includegraphics[width=\textwidth]{img/fig2.pdf} +% \includegraphics[width=0.35\textwidth]{img/fig2.pdf} +\begin{lstlisting}[language=python] +storage = Storage() + +# memoizing context +with storage: + for x in range(3): + y = f(x) + +In [1]: y # wrapped value +Out[1]: AtomRef(4, + hid='628...', + cid='a82...') +\end{lstlisting} \caption{} \end{subfigure} \begin{subfigure}{0.4\textwidth} \centering -\includegraphics[width=\textwidth]{img/fig3.pdf} +% \includegraphics[width=0.4\textwidth]{img/fig3.pdf} +\begin{lstlisting}[language=python] +with storage: + # just add more calls + # & reuse old results + for x in range(5): + y = f(x) + # unwrap for control flow + if storage.unwrap(y) > 5: + z = g(x, y) + +# the "program" is now end-to-end +# memoized & retraceable +\end{lstlisting} \caption{} \end{subfigure} \caption{Basic imperative usage of \texttt{mandala}. \textbf{(a)}: add the \texttt{@op} @@ -99,11 +140,11 @@ \section{Introduction} The rest of this paper presents the design and main functionalities of \texttt{mandala}, and is organized as follows: \begin{itemize} -\item In Section \ref{section:core-concepts}, we describe how memoization is +\item In \autoref{section:core-concepts}, we describe how memoization is designed, how this allows memoized calls to be composed and memoized results to be reused without storage duplication, and how this enables the \emph{retracing} pattern of interacting with computational artifacts. -\item In Section \ref{section:cf}, we introduce the concept of a +\item In \autoref{section:cf}, we introduce the concept of a \emph{computation frame}, which generalizes a dataframe by replacing columns with a computational graph, and rows with individual computations that (partially) follow this graph. Computation frames allow high-level exploration @@ -111,7 +152,7 @@ \section{Introduction} produced/used given values to the graph, deleting all computations that depend on the calls captured in the frame, and restricting the frame to a particular subgraph or subset of values with given properties. -\item In Section \ref{section:extra-features}, we describe some other features of +\item In \autoref{section:extra-features}, we describe some other features of \texttt{mandala} necessary to make it a practical tool, such as: \begin{itemize} \item Representing Python collections in a way transparent to the storage, so @@ -122,7 +163,7 @@ \section{Introduction} \end{itemize} \end{itemize} -Finally, we give an overview of related work in Section \ref{section:related-work}. +Finally, we give an overview of related work in \autoref{section:related-work}. \section{Core Concepts} \label{section:core-concepts} @@ -133,7 +174,7 @@ \subsection{Memoization and the Computational Graph} to avoid redundant computation. \texttt{mandala} uses \emph{automatic} memoization \citep{norvig1991techniques} which is applied via the combination of a decorator (\texttt{@op}) and a context manager which specifies the -\texttt{Storage} object to use (Figure \ref{fig:basic-usage}). The memoization +\texttt{Storage} object to use (\autoref{fig:basic-usage}). The memoization can optionally be made persistent to disk, which is what you would typically want in a long-running project. Any Python function can be memoized (as long as its inputs and outputs are serializable by the \texttt{joblib} library; see the @@ -227,8 +268,7 @@ \subsection{Motivation for the Design of Memoization} composition of \texttt{@op}s, it \textbf{automatically builds up a computational graph of the project}. Most data management tasks --- e.g., a frequent use case is getting a table of relationships between some variables --- are naturally -expressed as queries over this graph, as we will see in Section -\ref{section:cf}. +expressed as queries over this graph, as we will see in \autoref{section:cf}. \item It \textbf{organizes storage functionality around a familiar and flexible interface: the function call}. This automatically enforces the good practice of partitioning code into functions, and eliminates extra `accidental' code to @@ -242,9 +282,9 @@ \subsection{Motivation for the Design of Memoization} \item Referring to values without reference to the code that produced or used them becomes difficult, because from the point of view of storage the `identity' of a value is its place in the computational graph. We discuss practical ways to -overcome this in Section \ref{section:cf}. +overcome this in \autoref{section:cf}. \item Modifying \texttt{@op} functions requires care, as changes may invalidate -the stored computational graph. We discuss a versioning system that automates this process in Section \ref{subsection:versioning}. +the stored computational graph. We discuss a versioning system that automates this process in \autoref{subsection:versioning}. \end{itemize} \subsection{Retracing as a Versatile Imperative Interface to the Stored Computation Graph} @@ -256,8 +296,7 @@ \subsection{Retracing as a Versatile Imperative Interface to the Stored Computat way to interact with such a persisted computation is through \textbf{retracing}, which means stepping through memoized code with the purpose of resuming from a failure, loading intermediate values, or continuing from a particular point with -new computations. A small example of retracing is shown in Figure -\ref{fig:basic-usage} (c). +new computations. A small example of retracing is shown in \autoref{fig:basic-usage} (c). This pattern is simple yet powerful, as it allows the user to interact with the stored computation graph in a way that is adapted to their use case, and to @@ -270,52 +309,58 @@ \section{Computation Frames} \begin{figure}[htbp] \centering - \begin{minipage}[b]{0.48\textwidth} - \begin{subfigure}[b]{\textwidth} - \centering - \includegraphics[width=\textwidth]{img/fig4.pdf} - \caption{Continuing from Figure \ref{fig:basic-usage}, we first - create a computation frame from a single function \texttt{f}, then - expand it to include all calls that can be reached from the memoized - calls to \texttt{f} via their inputs/outputs, and finally convert - the computation frame into a dataframe. We see that this - automatically produces a computation graph corresponding to the - computations found.} - \label{fig:figure1} - \end{subfigure} - - \vspace{1em} - - \begin{subfigure}[b]{\textwidth} - \centering - \includegraphics[width=\textwidth]{img/fig5.pdf} - \caption{The output of the call to \texttt{.eval()} from the left - subfigure used to turn the computaiton frame into a dataframe. The - resulting table has columns for all variables and functions - appearing in the captured computation graph, and each row correspond - to a partial computation following this graph. The variable columns - contain values these variables take, whereas function columns - contain call objects representing the memoized calls to the - respective functions. We see that, because we call \texttt{g} - conditional on the output of \texttt{f}, some rows have nulls in the - \texttt{g} column.} - \label{fig:figure2} - \end{subfigure} - \end{minipage} - \hfill - \begin{minipage}[b]{0.45\textwidth} - \begin{subfigure}[b]{\textwidth} - \centering - \includegraphics[width=\textwidth]{img/cf.pdf} - \caption{A visualization of the computation frame from the previous - two subfigures. The red nodes indicate functions, and the blue nodes - indicate variables in the computation graph. Each edge is labeled - with the input/output name of the adjacent function. Nodes and edges - also show the number of \texttt{Ref}s and \texttt{Call}s they - represent.} - \label{fig:figure3} - \end{subfigure} - \end{minipage} + \begin{subfigure}[b]{\textwidth} + \centering + % \includegraphics[width=0.45\textwidth]{img/fig4.pdf} + \begin{lstlisting}[language=python] +In [1]: +# get the computation frame for f +storage.cf(f).\ + # add all computations reachable + # from calls to f + expand().\ + # extract as a dataframe + eval() +Out[1]: Extracting tuples from the +computation graph: + output_0 = f(x=x) + output_1 = g(y=output_0, x=x) + \end{lstlisting} + \caption{Continuing from \autoref{fig:basic-usage}, we first + create a computation frame from a single function \texttt{f}, then + expand it to include all calls that can be reached from the memoized + calls to \texttt{f} via their inputs/outputs, and finally convert + the computation frame into a dataframe. We see that this + automatically produces a computation graph corresponding to the + computations found.} + \label{fig:figure1} + \end{subfigure} + \begin{subfigure}[b]{\textwidth} + \centering + \includegraphics[width=0.80\linewidth]{img/fig5.pdf} + \caption{The output of the call to \texttt{.eval()} from the left + subfigure used to turn the computaiton frame into a dataframe. The + resulting table has columns for all variables and functions + appearing in the captured computation graph, and each row correspond + to a partial computation following this graph. The variable columns + contain values these variables take, whereas function columns + contain call objects representing the memoized calls to the + respective functions. We see that, because we call \texttt{g} + conditional on the output of \texttt{f}, some rows have nulls in the + \texttt{g} column.} + \label{fig:figure2} + \end{subfigure} + \begin{subfigure}[b]{\textwidth} + \centering + \includegraphics[width=0.45\textwidth]{img/cf.pdf} + \caption{A visualization of the computation frame from the previous + two subfigures. The red nodes indicate functions, and the blue nodes + indicate variables in the computation graph. Each edge is labeled + with the input/output name of the adjacent function. Nodes and edges + also show the number of \texttt{Ref}s and \texttt{Call}s they + represent.} + \label{fig:figure3} + \end{subfigure} \caption{Basic declarative usage of \texttt{mandala} and an example of computation frames.} \label{fig:cf} @@ -334,9 +379,8 @@ \subsection{Motivation and Intuition} \texttt{@op} calls into groups, where the calls in each group have an analogous role in the computation, and the groups form a high-level computational graph of variables (which represent groups of \texttt{Ref}s) and functions (groups of -\texttt{Call}s). The illustration in Figure \ref{fig:cf} (c) shows a -visualization of a computation frame extracted from the computations in Figure -\ref{fig:basic-usage}. +\texttt{Call}s). The illustration in \autoref{fig:cf} (c) shows a +visualization of a computation frame extracted from the computations in \autoref{fig:basic-usage}. This kind of organization is useful because it reflects how the user thinks about the computation, and allows them to tailor the exploration of the @@ -355,12 +399,12 @@ \subsection{Formal Definition} \label{subsection:cf-definition} -A computation frame (Figure \ref{fig:cf}) consists of the following data: +A computation frame (\autoref{fig:cf}) consists of the following data: \begin{itemize} \item \textbf{Computation graph}: a directed graph $G=(V,F,E)$ where $V$ are named variables and $F$ are named instances of \texttt{@op}-decorated functions. The edges $E$ are labeled with the input/output names of the adjacent functions. -An example is shown in Figure \ref{fig:cf} (c); +An example is shown in \autoref{fig:cf} (c); \item \textbf{Groups of \texttt{Ref}s and \texttt{Call}s}: for each variable $v\in V$, a set of (history IDs of) \texttt{Ref}s $R_v$, and for each function $f\in F$ with underlying \texttt{@op} $o_f$, a set of (history IDs of) \texttt{Call}s $C_f$; @@ -384,7 +428,7 @@ \subsection{Basic Usage} \item \textbf{Iteratively expanding the frame with functions that generated or used existing variables}: this is useful for exploring the computation graph in a particular direction, or for adding more context to a particular computation. -For example, in Figure \ref{fig:cf} (a), we start with a computation frame +For example, in \autoref{fig:cf} (a), we start with a computation frame containing only the calls to \texttt{f}, and then expand it to include all calls that can be reached from the memoized calls to \texttt{f} via their inputs/outputs, which adds the calls to \texttt{g} to the frame. @@ -394,7 +438,7 @@ \subsection{Basic Usage} \texttt{Ref}s in the frame's computational graph (i.e., those that are not inputs to any function in the frame), computing their computational history in the frame (grouped by variable), and joining the resulting tables over the -variables. This is shown in Figure \ref{fig:cf} (right). In particular, as shown +variables. This is shown in \autoref{fig:cf} (right). In particular, as shown in the example, this step may produce nulls, as the computation frame can contain computations that only partially follow the graph. \item \textbf{Performing high-level storage manipulations}: such as deleting all @@ -421,7 +465,21 @@ \subsection{Data Structures} \begin{wrapfigure}[18]{l}{0.45\textwidth} \centering - \includegraphics[width=\linewidth]{img/list.pdf} + % \includegraphics[width=0.45\linewidth]{img/list.pdf} + \begin{lstlisting}[language=python] +@op +def avg_items(xs: MList[int]) -> float: + return sum(xs) / len(xs) + +@op +def get_xs(n) -> MList[int]: + return list(range(n)) + +with storage: + xs = get_xs(10) + for i in range(2, 10, 2): + avg = avg_items(xs[:i]) + \end{lstlisting} \caption{Illustration of native collection memoization in \texttt{mandala}. The custom type annotation \texttt{MList[int]} is used to memoize a list of integers as a list of pointers to element \texttt{Ref}s.} @@ -435,7 +493,7 @@ \subsection{Data Structures} collections are naturally incorporated in the computation graph. 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