Emily Boudreaux
ba457e7d10
The initial prototype for this code used a number of bespoke so-called "prepared" operators to avoid recomputation of values which we could be sure would not change. However, this system was hard to scale. Here we implement an equivalent, but much more scalable, solution through a computational graph which keeps track of inputs and outputs to each node and only reruns the node when the inputs have changed. Note that this is a simplistic optimization scheme, we are do not check for example if the outputs change strongly or even at all with different inputs (generally that would require derivatives of the outputs wrt. each input) nor do we do any sort of graph optimization.
171 lines
9.7 KiB
C++
171 lines
9.7 KiB
C++
#pragma once
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/**
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* @file dependencies.hpp
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* @author Emily M. Boudreaux
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* @brief This file is a convenience header which includes all of the headers in the dependencies module. Further, we include the detailed
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* documentation for the dependencies module here.
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*
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*/
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#include "serif/utils/dependencies/keys.hpp"
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#include "serif/utils/dependencies/binding.hpp"
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#include "serif/utils/dependencies/key_list.hpp"
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#include "serif/utils/dependencies/key_list_operations.hpp"
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#include "serif/utils/dependencies/dependency_context.hpp"
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#include "serif/utils/dependencies/computation.hpp"
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#include "serif/utils/dependencies/computation_graph.hpp"
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#include "serif/utils/dependencies/observers.hpp"
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#include "serif/utils/dependencies/computation_cache.hpp"
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#include "serif/utils/dependencies/snapshot.hpp"
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/**
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* *Computational Graphs*
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*
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* Demonstration code for how to use the SERiF computational graph system
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* The broad idea here is that there are many calculations which depend on each other but which may not necessarily always need to be recomputed just
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* because another part of the model has changed. We model this as a combination of an acyclic computational graph, defined at compile time, and
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* a runtime cache of values. The general workflow is that a developer defines the "context" of the computational graph, think of this
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* as all of the inputs to the graph, or maybe more specifically, anything that could change due to some external event (e.g. a time step, a newton step, etc...)
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* This context is probably the most CS intensive part of this code as the developer needs to think about lifetimes and ownership.
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*
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* **Context**
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* Contexts are made with the make_dependency_context function, this accepts a variadic pack of bindings between a type and a runtime value.
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* You can generate these yourself using the binding functions and types; however, we provide two convenience wrappers which generally developers should consider
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* using.
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* - borrow: Used to bind a type to a reference to a value. This is useful for values which are owned by the caller and which the caller will manage the lifetime of (takes an lvalue).
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* - own: Used to bind a type to a value which is owned by the context (copies an lvalue or moves an rvalue)
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* Each of these functions accepts a type as a template parameter and a runtime value as an argument. The type is used as an ID, this is key to understand, if we write
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* \code{.cpp}
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* using namespace serif::utils::dependencies;
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*
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* double x{1.0};
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* auto context = make_dependency_context(borrow<keys::Input>(x));
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* \endcode
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*
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* we are not saying that x is of type keys::Input, this is clearly not the case. Rather, we are registering in the computational graph a binding
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* between the type keys::Input and the runtime value x. Think of this as naming the various inputs to the graph.
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*
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* **Computations**
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* After defining a context we can define a computation, each computation defines a node, while its ordered input keys define the incoming edges.
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* Consider the following example (assuming the context from above):
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*
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* \code{.cpp}
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* auto func = make_computation<keys::Output, InputList<keys::Input>>(
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* [](const double _x) -> double {
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* return _x * _x;
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* }
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* );
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* \endcode
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*
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* what we have defined here is a node which takes a single input tagged keys::Input and produces an output tagged as keys::Output. The input key
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* declares an edge from that source to this node. Other nodes can use keys::Output as an input, and a successfully prepared result can be read with get
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* or retained with share. These connections allow us to form a larger graph. Consider now the following code
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*
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* \code{.cpp}
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* using namespace serif::utils::dependencies;
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*
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* struct tags {
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* struct X{};
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* struct Y{};
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*
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* struct Output1{};
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* struct Output2{};
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* };
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*
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* double x{1.0};
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* double y{2.0};
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* auto context = make_dependency_context(borrow<tags::X>(x), borrow<tags::Y>(y));
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*
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* auto func1 = make_computation<tags::Output1, InputList<tags::X>>(
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* [](const double _x) -> double {
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* return _x * _x;
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* }
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* );
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*
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* auto func2 = make_computation<tags::Output2, InputList<tags::Output1, tags::Y>>(
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* [](const double _x, const double _y) -> double {
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* return _x + _y;
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* }
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* );
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* \endcode
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*
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* we have now defined two computations, the first takes a single input, whichever input is tagged as tags::X and produces an output tagged as tags::Output1.
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* The second computation takes two inputs, the first is the output of the first computation (lookup by the tag) and the second is whichever input is tagged as tags::Y,
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* and produces an output tagged as tags::Output2. Note that the order of arguments to the lambda must be the same as the order of the tags in the InputList, the
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* dependencies module will automatically order arguments based on the InputList. There is type checking to ensure that the types match, but if you have two
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* inputs of the same type that are flipped there is no way for the system to inspect that and warn you. Further, depending on your compiler and flags
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* there may be implicit casting between convertable types (e.g. int <-> double).
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*
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* @todo We may want to remove the ability to have implicit casting between types, this is a bit of a footgun and may lead to subtle bugs.
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*
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* @note This is primary goal of this system, split computations into small pieces with the ability to cache intermediate results and reuse
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* as many of those intermediate results as we can can prove is safe to do. The whole reason this system is so complex is that very purpose. There is a fundamental
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* problem with high dimensional stellar models, they are always going to be slow, therefore we need to accept some degree of complexity in order to make them tractable.
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*
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* **Computation Cache**
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* Once we have the computations we need to connect them together, generally use the helper function make_computation_cache which
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* takes a context and a variadic pack of computations. There are *a lot* of compile time checks which run to ensure that the graph is valid, the context
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* describes what is needed to compute the graph, and that each computation is valid. Generally if a static analysis error or compile error pops up here
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* it probably means there is a mistake in the definition of the context or the computations. I've tried to make these errors as clear as possible; however,
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* there is only so much that you can do with the C++ compiler. I recommend first checking, probably with a drawn-out diagram, that all the
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* tags are correct and what you expect.
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*
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* A developer can also choose to use the make_computation_graph function to explicitly name a graph. This
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* is useful when naming or reusing a collection of computations (make_computation_cache
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* calls make_computation_graph and then fixes the source types from the supplied context without retaining the context). Now consider the following code assuming the context and computations from above:
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*
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* \code{.cpp}
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* auto graph_cache = make_computation_cache(context, func1, func2);
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* \endcode
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*
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* At this point we have a cache which we can access various parts of; however, no computations have been run yet. To run a computation we must call try_prepare.
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*
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* try_prepare returns std::expected: ordinary rejection is a value, and no candidate results or snapshots are published on failure.
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* Exceptions from a callable or snapshot policy propagate with the same cache protection. Previously retained results remain valid,
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* but get and share require a successful current preparation. Always check the report before reading results.
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*
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* try_prepare takes the context and evaluates the requested nodes and their prerequisites, reusing unchanged results. By default every result
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* is requested, so the first call evaluates the entire graph. RequestedResults can restrict evaluation to a branch.
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*
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* \code{.cpp}
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* auto report = graph_cache.try_prepare(context);
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* if (!report) {
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* return; // Handle report.error() before reading the cache.
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* }
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*
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* double func1_result = graph_cache.template get<tags::Output1>();
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* double func2_result = graph_cache.template get<tags::Output2>();
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*
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* auto func1_status = report->template status<tags::Output1>();
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* auto func2_status = report->template status<tags::Output2>();
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*
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* std::string_view func1_status_string = preparation_status_name_map[func1_status]; // note this is an inline map defined in serif::utils::dependencies
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* std::string_view func2_status_string = preparation_status_name_map[func2_status];
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*
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* std::println("Func1 Result: {}, Func2 Result: {}, Func1 Status: {}, Func2 Status: {}", func1_result, func2_result, func1_status_string, func2_status_string);
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* \endcode
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*
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* Now what we could do is change only one part of the input context and call try_prepare again. Lets say we change only y, such that only func2 is stale.
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*
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* \code{.cpp}
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* y = 3.0;
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* report = graph_cache.try_prepare(context);
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* if (!report) {
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* return; // Handle report.error() before reading the cache.
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* }
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*
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* double func1_result = graph_cache.template get<tags::Output1>();
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* double func2_result = graph_cache.template get<tags::Output2>();
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*
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* auto func1_status = report->template status<tags::Output1>();
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* auto func2_status = report->template status<tags::Output2>();
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*
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* std::string_view func1_status_string = preparation_status_name_map[func1_status]; // note this is an inline map defined in serif::utils::dependencies
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* std::string_view func2_status_string = preparation_status_name_map[func2_status];
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*
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* std::println("Func1 Result: {}, Func2 Result: {}, Func1 Status: {}, Func2 Status: {}", func1_result, func2_result, func1_status_string, func2_status_string);
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* \endcode
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*/
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namespace serif::utils::dependencies{};
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