llvm-project

Author SHA1 Message Date

Author	SHA1	Message	Date
Philip Reames	6ef4505298	[funcattrs] Infer nosync from readnone and non-convergent This implements the most basic possible nosync inference. The choice of inference rule is taken from the comments in attributor and the discussion on the review of the change which introduced the nosync attribute (`0626367202`). This is deliberately minimal. As noted in code comments, I do plan to add a more robust inference which actually scans the function IR directly, but a) I need to do some refactoring of the attributor code to use common interfaces, and b) I wanted to get something in. I also wanted to minimize the "interesting" analysis discussion since that's time intensive. Context: This combines with existing nofree attribute inference to help prove dereferenceability in the ongoing deref-at-point semantics work. Differential Revision: https://reviews.llvm.org/D99749	2021-04-01 11:37:34 -07:00
Chandler Carruth	05ca5acc9e	[PM] Introduce a devirtualization iteration layer for the new PM. This is an orthogonal and separated layer instead of being embedded inside the pass manager. While it adds a small amount of complexity, it is fairly minimal and the composability and control seems worth the cost. The logic for this ends up being nicely isolated and targeted. It should be easy to experiment with different iteration strategies wrapped around the CGSCC bottom-up walk using this kind of facility. The mechanism used to track devirtualization is the simplest one I came up with. I think it handles most of the cases the existing iteration machinery handles, but I haven't done a very in depth analysis. It does however match the basic intended semantics, and we can tweak or tune its exact behavior incrementally as necessary. One thing that we may want to revisit is freshly building the value handle set on each iteration. While I don't think this will be a significant cost (it is strictly fewer value handles but more churn of value handes than the old call graph), it is conceivable that we'll want a somewhat more clever tracking mechanism. My hope is to layer that on as a follow up patch with data supporting any implementation complexity it adds. This code also provides for a basic count heuristic: if the number of indirect calls decreases and the number of direct calls increases for a given function in the SCC, we assume devirtualization is responsible. This matches the heuristics currently used in the legacy pass manager. Differential Revision: https://reviews.llvm.org/D23114 llvm-svn: 290665	2016-12-28 11:07:33 +00:00
Chandler Carruth	8882346842	[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing some mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a great name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the particular nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the highest entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks EVERYTHING. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a persistent set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a new, bottom SCC/RefSCC. - We also need the ability to skip SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip nodes from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then directly update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to invalidate the analyses associated with it. - We also need to invalidate analyses after we finish processing an SCC. But the analyses we need to invalidate here are only those for the newly updated SCC!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618	2016-08-24 09:37:14 +00:00

Philip Reames

6ef4505298

[funcattrs] Infer nosync from readnone and non-convergent

This implements the most basic possible nosync inference. The choice of inference rule is taken from the comments in attributor and the discussion on the review of the change which introduced the nosync attribute (0626367202).

This is deliberately minimal. As noted in code comments, I do plan to add a more robust inference which actually scans the function IR directly, but a) I need to do some refactoring of the attributor code to use common interfaces, and b) I wanted to get something in. I also wanted to minimize the "interesting" analysis discussion since that's time intensive.

Context: This combines with existing nofree attribute inference to help prove dereferenceability in the ongoing deref-at-point semantics work.

Differential Revision: https://reviews.llvm.org/D99749

2021-04-01 11:37:34 -07:00

Chandler Carruth

05ca5acc9e

[PM] Introduce a devirtualization iteration layer for the new PM.

This is an orthogonal and separated layer instead of being embedded
inside the pass manager. While it adds a small amount of complexity, it
is fairly minimal and the composability and control seems worth the
cost.

The logic for this ends up being nicely isolated and targeted. It should
be easy to experiment with different iteration strategies wrapped around
the CGSCC bottom-up walk using this kind of facility.

The mechanism used to track devirtualization is the simplest one I came
up with. I think it handles most of the cases the existing iteration
machinery handles, but I haven't done a *very* in depth analysis. It
does however match the basic intended semantics, and we can tweak or
tune its exact behavior incrementally as necessary. One thing that we
may want to revisit is freshly building the value handle set on each
iteration. While I don't think this will be a significant cost (it is
strictly fewer value handles but more churn of value handes than the old
call graph), it is conceivable that we'll want a somewhat more clever
tracking mechanism. My hope is to layer that on as a follow up patch
with data supporting any implementation complexity it adds.

This code also provides for a basic count heuristic: if the number of
indirect calls decreases and the number of direct calls increases for
a given function in the SCC, we assume devirtualization is responsible.
This matches the heuristics currently used in the legacy pass manager.

Differential Revision: https://reviews.llvm.org/D23114

llvm-svn: 290665

2016-12-28 11:07:33 +00:00

Chandler Carruth

8882346842

[PM] Introduce basic update capabilities to the new PM's CGSCC pass

manager, including both plumbing and logic to handle function pass
updates.

There are three fundamentally tied changes here:
1) Plumbing *some* mechanism for updating the CGSCC pass manager as the
   CG changes while passes are running.
2) Changing the CGSCC pass manager infrastructure to have support for
   the underlying graph to mutate mid-pass run.
3) Actually updating the CG after function passes run.

I can separate them if necessary, but I think its really useful to have
them together as the needs of #3 drove #2, and that in turn drove #1.

The plumbing technique is to extend the "run" method signature with
extra arguments. We provide the call graph that intrinsically is
available as it is the basis of the pass manager's IR units, and an
output parameter that records the results of updating the call graph
during an SCC passes's run. Note that "...UpdateResult" isn't a *great*
name here... suggestions very welcome.

I tried a pretty frustrating number of different data structures and such
for the innards of the update result. Every other one failed for one
reason or another. Sometimes I just couldn't keep the layers of
complexity right in my head. The thing that really worked was to just
directly provide access to the underlying structures used to walk the
call graph so that their updates could be informed by the *particular*
nature of the change to the graph.

The technique for how to make the pass management infrastructure cope
with mutating graphs was also something that took a really, really large
number of iterations to get to a place where I was happy. Here are some
of the considerations that drove the design:

- We operate at three levels within the infrastructure: RefSCC, SCC, and
  Node. In each case, we are working bottom up and so we want to
  continue to iterate on the "lowest" node as the graph changes. Look at
  how we iterate over nodes in an SCC running function passes as those
  function passes mutate the CG. We continue to iterate on the "lowest"
  SCC, which is the one that continues to contain the function just
  processed.

- The call graph structure re-uses SCCs (and RefSCCs) during mutation
  events for the *highest* entry in the resulting new subgraph, not the
  lowest. This means that it is necessary to continually update the
  current SCC or RefSCC as it shifts. This is really surprising and
  subtle, and took a long time for me to work out. I actually tried
  changing the call graph to provide the opposite behavior, and it
  breaks *EVERYTHING*. The graph update algorithms are really deeply
  tied to this particualr pattern.

- When SCCs or RefSCCs are split apart and refined and we continually
  re-pin our processing to the bottom one in the subgraph, we need to
  enqueue the newly formed SCCs and RefSCCs for subsequent processing.
  Queuing them presents a few challenges:
  1) SCCs and RefSCCs use wildly different iteration strategies at
     a high level. We end up needing to converge them on worklist
     approaches that can be extended in order to be able to handle the
     mutations.
  2) The order of the enqueuing need to remain bottom-up post-order so
     that we don't get surprising order of visitation for things like
     the inliner.
  3) We need the worklists to have set semantics so we don't duplicate
     things endlessly. We don't need a *persistent* set though because
     we always keep processing the bottom node!!!! This is super, super
     surprising to me and took a long time to convince myself this is
     correct, but I'm pretty sure it is... Once we sink down to the
     bottom node, we can't re-split out the same node in any way, and
     the postorder of the current queue is fixed and unchanging.
  4) We need to make sure that the "current" SCC or RefSCC actually gets
     enqueued here such that we re-visit it because we continue
     processing a *new*, *bottom* SCC/RefSCC.

- We also need the ability to *skip* SCCs and RefSCCs that get merged
  into a larger component. We even need the ability to skip *nodes* from
  an SCC that are no longer part of that SCC.

This led to the design you see in the patch which uses SetVector-based
worklists. The RefSCC worklist is always empty until an update occurs
and is just used to handle those RefSCCs created by updates as the
others don't even exist yet and are formed on-demand during the
bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and
we push new SCCs onto it and blacklist existing SCCs on it to get the
desired processing.

We then *directly* update these when updating the call graph as I was
never able to find a satisfactory abstraction around the update
strategy.

Finally, we need to compute the updates for function passes. This is
mostly used as an initial customer of all the update mechanisms to drive
their design to at least cover some real set of use cases. There are
a bunch of interesting things that came out of doing this:

- It is really nice to do this a function at a time because that
  function is likely hot in the cache. This means we want even the
  function pass adaptor to support online updates to the call graph!

- To update the call graph after arbitrary function pass mutations is
  quite hard. We have to build a fairly comprehensive set of
  data structures and then process them. Fortunately, some of this code
  is related to the code for building the cal graph in the first place.
  Unfortunately, very little of it makes any sense to share because the
  nature of what we're doing is so very different. I've factored out the
  one part that made sense at least.

- We need to transfer these updates into the various structures for the
  CGSCC pass manager. Once those were more sanely worked out, this
  became relatively easier. But some of those needs necessitated changes
  to the LazyCallGraph interface to make it significantly easier to
  extract the changed SCCs from an update operation.

- We also need to update the CGSCC analysis manager as the shape of the
  graph changes. When an SCC is merged away we need to clear analyses
  associated with it from the analysis manager which we didn't have
  support for in the analysis manager infrsatructure. New SCCs are easy!
  But then we have the case that the original SCC has its shape changed
  but remains in the call graph. There we need to *invalidate* the
  analyses associated with it.

- We also need to invalidate analyses after we *finish* processing an
  SCC. But the analyses we need to invalidate here are *only those for
  the newly updated SCC*!!! Because we only continue processing the
  bottom SCC, if we split SCCs apart the original one gets invalidated
  once when its shape changes and is not processed farther so its
  analyses will be correct. It is the bottom SCC which continues being
  processed and needs to have the "normal" invalidation done based on
  the preserved analyses set.

All of this is mostly background and context for the changes here.

Many thanks to all the reviewers who helped here. Especially Sanjoy who
caught several interesting bugs in the graph algorithms, David, Sean,
and others who all helped with feedback.

Differential Revision: http://reviews.llvm.org/D21464

llvm-svn: 279618

2016-08-24 09:37:14 +00:00

3 Commits