This module contains the infrastructure for managing the incremental compilation dependency graph. This README aims to explain how it ought to be used. In this document, we'll first explain the overall strategy, and then share some tips for handling specific scenarios.
The high-level idea is that we want to instrument the compiler to track which parts of the AST and other IR are read/written by what. This way, when we come back later, we can look at this graph and determine what work needs to be redone.
The nodes of the graph are defined by the enum DepNode. They represent one of three things:
Hir(DefId)) represent the HIR input itself.ItemSignature(DefId)) represent some computed information about a particular item.CoherenceCheckImpl(DefId)) represent some procedure that is executing. Usually this procedure is performing some kind of check for errors. You can think of them as computed values where the value being computed is () (and the value may fail to be computed, if an error results).An edge N1 -> N2 is added between two nodes if either:
N1 is used to compute N2;N1 is read by the procedure N2;N1 writes the value N2.The latter two conditions are equivalent to the first one if you think of procedures as values.
There is a very general strategy to ensure that you have a correct, if sometimes overconservative, dependency graph. The two main things you have to do are (a) identify shared state and (b) identify the current tasks.
Identify “shared state” that will be written by one pass and read by another. In particular, we need to identify shared state that will be read “across items” -- that is, anything where changes in one item could invalidate work done for other items. So, for example:
Put another way: if the HIR for an item changes, we are going to recompile that item for sure. But we need the dep tracking map to tell us what else we have to recompile. Shared state is anything that is used to communicate results from one item to another.
The dep graph always tracks a current task: this is basically the DepNode that the compiler is computing right now. Typically it would be a procedure node, but it can also be a data node (as noted above, the two are kind of equivalent).
You set the current task by calling dep_graph.in_task(node). For example:
let _task = tcx.dep_graph.in_task(DepNode::Privacy);
Now all the code until _task goes out of scope will be considered part of the “privacy task”.
The tasks are maintained in a stack, so it is perfectly fine to nest one task within another. Because pushing a task is considered to be computing a value, when you nest a task N2 inside of a task N1, we automatically add an edge N2 -> N1 (since N1 presumably needed the result of N2 to complete):
let _n1 = tcx.dep_graph.in_task(DepNode::N1); let _n2 = tcx.dep_graph.in_task(DepNode::N2); // this will result in an edge N1 -> n2
Although it is rarely needed, you can also push a special “ignore” task:
let _ignore = tc.dep_graph.in_ignore();
This will cause all read/write edges to be ignored until it goes out of scope or until something else is pushed. For example, we could suppress the edge between nested tasks like so:
let _n1 = tcx.dep_graph.in_task(DepNode::N1); let _ignore = tcx.dep_graph.in_ignore(); let _n2 = tcx.dep_graph.in_task(DepNode::N2); // now no edge is added
We need to identify what shared state is read/written by the current task as it executes. The most fundamental way of doing that is to invoke the read and write methods on DepGraph:
// Adds an edge from DepNode::Hir(some_def_id) to the current task tcx.dep_graph.read(DepNode::Hir(some_def_id)) // Adds an edge from the current task to DepNode::ItemSignature(some_def_id) tcx.dep_graph.write(DepNode::ItemSignature(some_def_id))
However, you should rarely need to invoke those methods directly. Instead, the idea is to encapsulate shared state into some API that will invoke read and write automatically. The most common way to do this is to use a DepTrackingMap, described in the next section, but any sort of abstraction barrier will do. In general, the strategy is that getting access to information implicitly adds an appropriate read. So, for example, when you use the dep_graph::visit_all_items_in_krate helper method, it will visit each item X, start a task Foo(X) for that item, and automatically add an edge Hir(X) -> Foo(X). This edge is added because the code is being given access to the HIR node for X, and hence it is expected to read from it. Similarly, reading from the tcache map for item X (which is a DepTrackingMap, described below) automatically invokes dep_graph.read(ItemSignature(X)).
To make this strategy work, a certain amount of indirection is required. For example, modules in the HIR do not have direct pointers to the items that they contain. Rather, they contain node-ids -- one can then ask the HIR map for the item with a given node-id. This gives us an opportunity to add an appropriate read edge.
One time when you may need to call read and write directly is when you push a new task onto the stack, either by calling in_task as shown above or indirectly, such as with the memoize pattern described below. In that case, any data that the task has access to from the surrounding environment must be explicitly “read”. For example, in librustc_typeck, the collection code visits all items and, among other things, starts a subtask producing its signature (what follows is simplified pseudocode, of course):
fn visit_item(item: &hir::Item) { // Here, current subtask is "Collect(X)", and an edge Hir(X) -> Collect(X) // has automatically been added by `visit_all_items_in_krate`. let sig = signature_of_item(item); } fn signature_of_item(item: &hir::Item) { let def_id = tcx.map.local_def_id(item.id); let task = tcx.dep_graph.in_task(DepNode::ItemSignature(def_id)); tcx.dep_graph.read(DepNode::Hir(def_id)); // <-- the interesting line ... }
Here you can see that, in signature_of_item, we started a subtask corresponding to producing the ItemSignature. This subtask will read from item -- but it gained access to item implicitly. This means that if it just reads from item, there would be missing edges in the graph:
Hir(X) --+ // added by the explicit call to `read` | | | +---> ItemSignature(X) -> Collect(X) | ^ | | +---------------------------------+ // added by `visit_all_items_in_krate`
In particular, the edge from Hir(X) to ItemSignature(X) is only present because we called read ourselves when entering the ItemSignature(X) task.
So, the rule of thumb: when entering a new task yourself, register reads on any shared state that you inherit. (This actually comes up fairly infrequently though: the main place you need caution is around memoization.)
DepTrackingMap is a particularly convenient way to correctly store shared state. A DepTrackingMap is a special hashmap that will add edges automatically when get and insert are called. The idea is that, when you get/insert a value for the key K, we will add an edge from/to the node DepNode::Variant(K) (for some variant specific to the map).
Each DepTrackingMap is parameterized by a special type M that implements DepTrackingMapConfig; this trait defines the key and value types of the map, and also defines a fn for converting from the key to a DepNode label. You don't usually have to muck about with this by hand, there is a macro for creating it. You can see the complete set of DepTrackingMap definitions in librustc/middle/ty/maps.rs.
As an example, let's look at the adt_defs map. The adt_defs map maps from the def-id of a struct/enum to its AdtDef. It is defined using this macro:
dep_map_ty! { AdtDefs: ItemSignature(DefId) -> ty::AdtDefMaster<'tcx> } // ~~~~~~~ ~~~~~~~~~~~~~ ~~~~~ ~~~~~~~~~~~~~~~~~~~~~~ // | | Key type Value type // | DepNode variant // Name of map id type
this indicates that a map id type AdtDefs will be created. The key of the map will be a DefId and value will be ty::AdtDefMaster<'tcx>. The DepNode will be created by DepNode::ItemSignature(K) for a given key.
Once that is done, you can just use the DepTrackingMap like any other map:
let mut map: DepTrackingMap<M> = DepTrackingMap::new(dep_graph); map.insert(key, value); // registers dep_graph.write map.get(key; // registers dep_graph.read
One particularly interesting case is memoization. If you have some shared state that you compute in a memoized fashion, the correct thing to do is to define a RefCell<DepTrackingMap> for it and use the memoize helper:
map.memoize(key, || /* compute value */)
This will create a graph that looks like
... -> MapVariant(key) -> CurrentTask
where MapVariant is the DepNode variant that the map is associated with, and ... are whatever edges the /* compute value */ closure creates.
In particular, using the memoize helper is much better than writing the obvious code yourself:
if let Some(result) = map.get(key) {
return result;
}
let value = /* compute value */;
map.insert(key, value);
If you write that code manually, the dependency graph you get will include artificial edges that are not necessary. For example, imagine that two tasks, A and B, both invoke the manual memoization code, but A happens to go first. The resulting graph will be:
... -> A -> MapVariant(key) -> B
~~~~~~~~~~~~~~~~~~~~~~~~~~~ // caused by A writing to MapVariant(key)
~~~~~~~~~~~~~~~~~~~~ // caused by B reading from MapVariant(key)
This graph is not wrong, but it encodes a path from A to B that should not exist. In contrast, using the memoized helper, you get:
... -> MapVariant(key) -> A
|
+----------> B
which is much cleaner.
Be aware though that the closure is executed with MapVariant(key) pushed onto the stack as the current task! That means that you must add explicit read calls for any shared state that it accesses implicitly from its environment. See the section on “explicit calls to read and write when starting a new subtask” above for more details.
Certainly, you need at least one task on the stack: any attempt to read or write shared state will panic if there is no current task. But where does it make sense to introduce subtasks? The basic rule is that a subtask makes sense for any discrete unit of work you may want to skip in the future. Adding a subtask separates out the reads/writes from that particular subtask versus the larger context. An example: you might have a ‘meta’ task for all of borrow checking, and then subtasks for borrow checking individual fns. (Seen in this light, memoized computations are just a special case where we may want to avoid redoing the work even within the context of one compilation.)
The other case where you might want a subtask is to help with refining the reads/writes for some later bit of work that needs to be memoized. For example, we create a subtask for type-checking the body of each fn. However, in the initial version of incr. comp. at least, we do not expect to actually SKIP type-checking -- we only expect to skip trans. However, it‘s still useful to create subtasks for type-checking individual items, because, otherwise, if a fn sig changes, we won’t know which callers are affected -- in fact, because the graph would be so coarse, we‘d just have to retrans everything, since we can’t distinguish which fns used which fn sigs.
There are various ways to write tests against the dependency graph. The simplest mechanism are the #[rustc_if_this_changed] and #[rustc_then_this_would_need] annotations. These are used in compile-fail tests to test whether the expected set of paths exist in the dependency graph. As an example, see src/test/compile-fail/dep-graph-caller-callee.rs.
The idea is that you can annotate a test like:
#[rustc_if_this_changed] fn foo() { } #[rustc_then_this_would_need(TypeckItemBody)] //~ ERROR OK fn bar() { foo(); } #[rustc_then_this_would_need(TypeckItemBody)] //~ ERROR no path fn baz() { }
This will check whether there is a path in the dependency graph from Hir(foo) to TypeckItemBody(bar). An error is reported for each #[rustc_then_this_would_need] annotation that indicates whether a path exists. //~ ERROR annotations can then be used to test if a path is found (as demonstrated above).
The compiler is also capable of dumping the dependency graph for your debugging pleasure. To do so, pass the -Z dump-dep-graph flag. The graph will be dumped to dep_graph.{txt,dot} in the current directory. You can override the filename with the RUST_DEP_GRAPH environment variable.
Frequently, though, the full dep graph is quite overwhelming and not particularly helpful. Therefore, the compiler also allows you to filter the graph. You can filter in three ways:
To filter, use the RUST_DEP_GRAPH_FILTER environment variable, which should look like one of the following:
source_filter // nodes originating from source_filter -> target_filter // nodes that can reach target_filter source_filter -> target_filter // nodes in between source_filter and target_filter
source_filter and target_filter are a &-separated list of strings. A node is considered to match a filter if all of those strings appear in its label. So, for example:
RUST_DEP_GRAPH_FILTER='-> TypeckItemBody'
would select the predecessors of all TypeckItemBody nodes. Usually though you want the TypeckItemBody node for some particular fn, so you might write:
RUST_DEP_GRAPH_FILTER='-> TypeckItemBody & bar'
This will select only the TypeckItemBody nodes for fns with bar in their name.
Perhaps you are finding that when you change foo you need to re-type-check bar, but you don't think you should have to. In that case, you might do:
RUST_DEP_GRAPH_FILTER='Hir&foo -> TypeckItemBody & bar'
This will dump out all the nodes that lead from Hir(foo) to TypeckItemBody(bar), from which you can (hopefully) see the source of the erroneous edge.