MultisampledNight
3e208335c3
Very hacky, but this is enough to be finished with the graph IR for now.
353 lines
13 KiB
Rust
353 lines
13 KiB
Rust
use std::num::NonZeroUsize;
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use instruction::SocketCount;
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use serde::{Deserialize, Serialize};
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pub mod id;
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pub mod instruction;
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pub mod semi_human;
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pub type Map<K, V> = std::collections::BTreeMap<K, V>;
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pub type Set<T> = std::collections::BTreeSet<T>;
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/// Gives you a super well typed graph IR for a given human-readable repr.
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///
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/// Look at [`semi_human::GraphIr`] and the test files in the repo at `testfiles/`
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/// to see what the RON should look like.
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/// No, we don't want you to write out [`GraphIr`] in full by hand.
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/// That's something for the machines to do.
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///
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/// # Errors
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///
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/// Returns an error if the parsed source is not a valid human-readable graph IR.
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pub fn from_ron(source: &str) -> ron::error::SpannedResult<GraphIr> {
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let human_repr: semi_human::GraphIr = ron::from_str(source)?;
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Ok(human_repr.into())
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}
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/// The toplevel representation of a whole pipeline.
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///
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/// # DAGs
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///
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/// Pipelines may not be fully linear. They may branch out and recombine later on.
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/// As such, the representation for them which is currently used is a
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/// [**D**irected **A**cyclic **G**raph](https://en.wikipedia.org/wiki/Directed_acyclic_graph).
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///
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/// For those who are already familiar with graphs, a DAG is one, except that:
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///
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/// - It is **directed**: Edges have a direction they point to.
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/// In this case, edges point from the outputs of streamers to inputs of consumers.
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/// - It is **acyclic**: Those directed edges may not form loops.
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/// In other words, if one follows edges only in their direction, it must be impossible
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/// to come back to an already visited node.
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///
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/// Here, if an edge points from _A_ to _B_ (`A --> B`),
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/// then _A_ is called a **dependency** or an **input source** of _B_,
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/// and _B_ is called a **dependent** or an **output target** of _A_.
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///
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/// The DAG also enables another neat operation:
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/// [Topological sorting](https://en.wikipedia.org/wiki/Topological_sorting).
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/// This allows to put the entire graph into a linear list,
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/// where it's guaranteed that once a vertex is visited,
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/// all dependencies of it will have been visited already as well.
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///
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/// The representation used here in specific is a bit more complicated,
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/// since **instructions** directly aren't just connected to one another,
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/// but their **sockets** are instead.
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///
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/// So the vertices of the DAG are the **sockets**
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/// (which are either [`id::Input`] or [`id::Output`] depending on the direction),
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/// and each **socket** in turn belongs to an **instruction**.
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///
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/// # Usage
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///
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/// - If you want to build one from scratch,
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/// add a few helper methods like
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/// constructing an empty one,
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/// adding instructions and
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/// adding edges
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/// - If you want to construct one from an existing repr,
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/// maybe you want to use [`semi_human::GraphIr`].
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///
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/// # Storing additional data
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///
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/// Chances are the graph IR seems somewhat fit to put metadata in it.
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/// However, most likely you're interacting in context of some other system,
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/// and also want to manage and index that data on your own.
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///
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/// As such, consider using _secondary_ maps instead.
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/// That is, store in a data structure _you_ own a mapping
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/// from [`id`]s
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/// to whatever data you need.
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#[derive(Clone, Debug, Default, PartialEq, Eq, Deserialize, Serialize)]
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pub struct GraphIr {
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/// "Backbone" storage of all **instruction** IDs to
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/// what **kind of instruction** they are.
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instructions: Map<id::Instruction, instruction::Kind>,
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/// How the data flows forward. **Dependencies** map to **dependents** here.
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edges: Map<id::Output, Set<id::Input>>,
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/// How the data flows backward. **Dependents** map to **dependencies** here.
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rev_edges: Map<id::Input, id::Output>,
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}
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// TODO: this impl block, but actually the whole module, screams for tests
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impl GraphIr {
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/// Look "backwards" in the graph,
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/// and find out what instructions need to be done before this one.
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/// The input slots are visited in order.
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///
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/// - The iterator returns individually [`Some`]`(`[`None`]`)` if the corresponding slot is
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/// not connected.
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///
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/// The same caveats as for [`GraphIr::resolve`] apply.
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#[must_use]
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pub fn input_sources(
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&self,
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subject: &id::Instruction,
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) -> Option<impl Iterator<Item = Option<&id::Output>> + '_> {
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let (subject, kind) = self.instructions.get_key_value(subject)?;
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let SocketCount { inputs, .. } = kind.socket_count();
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Some((0..inputs).map(|idx| {
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let input = id::Input(socket(subject, idx));
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self.rev_edges.get(&input)
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}))
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}
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/// Look "forwards" in the graph to see what other instructions this instruction feeds into.
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///
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/// The output slots represent the top-level iterator,
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/// and each one's connections are emitted one level below.
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///
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/// Just [`Iterator::flatten`] if you are not interested in the slots.
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///
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/// The same caveats as for [`GraphIr::resolve`] apply.
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#[must_use]
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pub fn output_targets(
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&self,
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subject: &id::Instruction,
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) -> Option<impl Iterator<Item = Option<&Set<id::Input>>> + '_> {
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let (subject, kind) = self.instructions.get_key_value(subject)?;
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let SocketCount { outputs, .. } = kind.socket_count();
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Some((0..outputs).map(|idx| {
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let output = id::Output(socket(subject, idx));
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self.edges.get(&output)
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}))
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}
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/// Returns the instruction corresponding to the given ID.
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/// Returns [`None`] if there is no such instruction in this graph IR.
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///
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/// Theoretically this could be fixed easily at the expense of some memory
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/// by just incrementing and storing some global counter,
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/// however, at the moment there's no compelling reason
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/// to actually have multiple [`GraphIr`]s at one point in time.
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/// Open an issue if that poses a problem for you.
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#[must_use]
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pub fn resolve<'ir>(&'ir self, subject: &id::Instruction) -> Option<InstructionRef<'ir>> {
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let (id, kind) = self.instructions.get_key_value(subject)?;
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let input_sources = self.input_sources(subject)?.collect();
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let output_targets = self.output_targets(subject)?.collect();
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Some(InstructionRef {
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id,
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kind,
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input_sources,
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output_targets,
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})
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}
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/// Returns the instruction this input belongs to.
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///
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/// The same caveats as for [`GraphIr::resolve`] apply.
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#[must_use]
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pub fn owner_of_input<'ir>(&'ir self, input: &id::Input) -> Option<InstructionRef<'ir>> {
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self.resolve(&input.socket().belongs_to)
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}
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/// Returns the instruction this output belongs to.
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///
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/// The same caveats as for [`GraphIr::resolve`] apply.
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#[must_use]
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pub fn owner_of_output<'ir>(&'ir self, output: &id::Output) -> Option<InstructionRef<'ir>> {
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self.resolve(&output.socket().belongs_to)
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}
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/// Returns the order in which the instructions could be visited
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/// in order to ensure that all dependencies are resolved
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/// before a vertex is visited.
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///
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/// # Panics
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///
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/// Panics if there are any cycles in the IR, as it needs to be a DAG.
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#[must_use]
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// yes, this function could probably return an iterator and be lazy
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// no, not today
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pub fn topological_sort(&self) -> Vec<InstructionRef> {
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// count how many incoming edges each vertex has
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let mut nonzero_input_counts: Map<_, NonZeroUsize> =
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self.rev_edges
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.iter()
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.fold(Map::new(), |mut count, (input, _)| {
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let _ = *count
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.entry(input.socket().belongs_to.clone())
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.and_modify(|count| *count = count.saturating_add(1))
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.or_insert(NonZeroUsize::MIN);
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count
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});
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// are there any unconnected ones we could start with?
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// TODO: experiment if a VecDeque with some ordering fun is digested better by the executor
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let no_inputs: Vec<_> = {
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let nonzero: Set<_> = nonzero_input_counts.keys().collect();
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let all: Set<_> = self.instructions.keys().collect();
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all.difference(&nonzero).copied().cloned().collect()
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};
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// then let's find the order!
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let mut order = Vec::new();
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let mut active_queue = no_inputs;
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while let Some(current) = active_queue.pop() {
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// now that this vertex is visited and resolved,
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// make sure all dependents notice that
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let dependents = self
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.output_targets(¤t)
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.expect("graph to be consistent")
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.flatten()
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.flatten();
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for dependent_input in dependents {
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let dependent = &dependent_input.socket().belongs_to;
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// how many inputs are connected to this dependent without us?
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let count = nonzero_input_counts
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.get_mut(dependent)
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.expect("connected output must refer to non-zero input");
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let new = NonZeroUsize::new(count.get() - 1);
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if let Some(new) = new {
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// aww, still some
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*count = new;
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continue;
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}
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// none, that means this one is free now! let's throw it onto the active queue then
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let (now_active, _) = nonzero_input_counts
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.remove_entry(dependent)
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.expect("connected output must refer to non-zero input");
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active_queue.push(now_active);
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}
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// TODO: check if this instruction is "well-fed", that is, has all the inputs it needs,
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// and if not, panic
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order.push(self.resolve(¤t).expect("graph to be consistent"));
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}
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assert!(
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nonzero_input_counts.is_empty(),
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concat!(
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"topological sort didn't cover all instructions\n",
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"either there are unconnected inputs, or there is a cycle\n",
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"unresolved instructions:\n",
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"{:#?}"
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),
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nonzero_input_counts,
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);
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order
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}
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}
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/// Constructs an [`id::Socket`] a bit more tersely.
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fn socket(id: &id::Instruction, idx: u16) -> id::Socket {
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id::Socket {
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belongs_to: id.clone(),
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idx: id::SocketIdx(idx),
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}
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}
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/// A full instruction bundeled in context, with its inputs and outputs.
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#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord)]
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pub struct Instruction {
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pub id: id::Instruction,
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pub kind: instruction::Kind,
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// can't have these two public since then a user might corrupt their length
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input_sources: Vec<Option<id::Output>>,
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output_targets: Vec<Set<id::Input>>,
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}
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impl Instruction {
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/// Where this instruction gets its inputs from.
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///
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/// [`None`] means that this input is unfilled,
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/// and must be filled before the instruction can be ran.
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#[must_use]
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pub fn input_sources(&self) -> &[Option<id::Output>] {
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&self.input_sources
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}
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/// To whom outputs are sent.
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#[must_use]
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pub fn output_targets(&self) -> &[Set<id::Input>] {
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&self.output_targets
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}
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}
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/// [`Instruction`], but every single field is borrowed instead.
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/// See its docs.
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///
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/// Use the [`From`] impl to handily convert into an [`Instruction`].
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/// The other way around is unlikely to be wanted — since you already have an [`Instruction`],
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/// chances are you just want to take a reference (`&`) of it.
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#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord)]
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pub struct InstructionRef<'ir> {
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pub id: &'ir id::Instruction,
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pub kind: &'ir instruction::Kind,
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input_sources: Vec<Option<&'ir id::Output>>,
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output_targets: Vec<Option<&'ir Set<id::Input>>>,
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}
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impl<'ir> InstructionRef<'ir> {
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/// Where this instruction gets its inputs from.
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///
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/// [`None`] means that this input is unfilled,
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/// and must be filled before the instruction can be ran.
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#[must_use]
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pub fn input_sources(&self) -> &[Option<&'ir id::Output>] {
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&self.input_sources
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}
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/// To whom outputs are sent.
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#[must_use]
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pub fn output_targets(&self) -> &[Option<&'ir Set<id::Input>>] {
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&self.output_targets
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}
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}
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// would love to use ToOwned but Rust has no specialization yet
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// and it'd hurt a blanket impl of ToOwned otherwise
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impl From<InstructionRef<'_>> for Instruction {
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fn from(source: InstructionRef<'_>) -> Self {
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Self {
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id: source.id.clone(),
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kind: source.kind.clone(),
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input_sources: source
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.input_sources
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.into_iter()
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.map(Option::<&_>::cloned)
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.collect(),
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output_targets: source
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.output_targets
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.into_iter()
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.map(|outputs| outputs.map(Clone::clone).unwrap_or_default())
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.collect(),
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}
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}
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}
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