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During estimation of unrolling effect we should be able to propagate
constants through casts.
Differential Revision: http://reviews.llvm.org/D10207
llvm-svn: 242257
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Enable runtime unrolling for loops with unroll count metadata ("#pragma unroll N")
and a runtime trip count. Also, do not unroll loops with unroll full metadata if the
loop has a runtime loop count. Previously, such loops would be unrolled with a
very large threshold (pragma-unroll-threshold) if runtime unrolled happened to be
enabled resulting in a very large (and likely unwise) unroll factor.
llvm-svn: 242047
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Apparently, the style needs to be agreed upon first.
llvm-svn: 240390
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The patch is generated using this command:
tools/clang/tools/extra/clang-tidy/tool/run-clang-tidy.py -fix \
-checks=-*,llvm-namespace-comment -header-filter='llvm/.*|clang/.*' \
llvm/lib/
Thanks to Eugene Kosov for the original patch!
llvm-svn: 240137
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llvm-svn: 239565
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heuristic.
Summary:
Using some SCEV functionality helped to entirely remove SCEVCache class and FindConstantPointers SCEV visitor.
Also, this makes the code more universal - I'll take advandate of it in next patches where I start handling additional types of instructions.
Test Plan: Tests would be submitted in subsequent patches.
Reviewers: atrick, chandlerc
Reviewed By: atrick, chandlerc
Subscribers: atrick, llvm-commits
Differential Revision: http://reviews.llvm.org/D10205
llvm-svn: 239282
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Summary:
canUnrollCompletely takes `unsigned` values for `UnrolledCost` and
`RolledDynamicCost` but is passed in `uint64_t`s that are silently
truncated. Because of this, when `UnrolledSize` is a large integer
that has a small remainder with UINT32_MAX, LLVM tries to completely
unroll loops with high trip counts.
Reviewers: mzolotukhin, chandlerc
Subscribers: llvm-commits
Differential Revision: http://reviews.llvm.org/D10293
llvm-svn: 239218
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The new naming is (to me) much easier to understand. Here is a summary
of the new state of the world:
- '*Threshold' is the threshold for full unrolling. It is measured
against the estimated unrolled cost as computed by getUserCost in TTI
(or CodeMetrics, etc). We will exceed this threshold when unrolling
loops where unrolling exposes a significant degree of simplification
of the logic within the loop.
- '*PercentDynamicCostSavedThreshold' is the percentage of the loop's
estimated dynamic execution cost which needs to be saved by unrolling
to apply a discount to the estimated unrolled cost.
- '*DynamicCostSavingsDiscount' is the discount applied to the estimated
unrolling cost when the dynamic savings are expected to be high.
When actually analyzing the loop, we now produce both an estimated
unrolled cost, and an estimated rolled cost. The rolled cost is notably
a dynamic estimate based on our analysis of the expected execution of
each iteration.
While we're still working to build up the infrastructure for making
these estimates, to me it is much more clear *how* to make them better
when they have reasonably descriptive names. For example, we may want to
apply estimated (from heuristics or profiles) dynamic execution weights
to the *dynamic* cost estimates. If we start doing that, we would also
need to track the static unrolled cost and the dynamic unrolled cost, as
only the latter could reasonably be weighted by profile information.
This patch is sadly not without functionality change for the new unroll
analysis logic. Buried in the heuristic management were several things
that surprised me. For example, we never subtracted the optimized
instruction count off when comparing against the unroll heursistics!
I don't know if this just got lost somewhere along the way or what, but
with the new accounting of things, this is much easier to keep track of
and we use the post-simplification cost estimate to compare to the
thresholds, and use the dynamic cost reduction ratio to select whether
we can exceed the baseline threshold.
The old values of these flags also don't necessarily make sense. My
impression is that none of these thresholds or discounts have been tuned
yet, and so they're just arbitrary placehold numbers. As such, I've not
bothered to adjust for the fact that this is now a discount and not
a tow-tier threshold model. We need to tune all these values once the
logic is ready to be enabled.
Differential Revision: http://reviews.llvm.org/D9966
llvm-svn: 239164
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lazily built.
Also, make it a much more generic SCEV cache, which today exposes only
a reduced GEP model description but could be extended in the future to
do other profitable caching of SCEV information.
llvm-svn: 238124
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accumulating estimated cost, and other loop-centric logic from the logic
used to analyze instructions in a particular iteration.
This makes the visitor very narrow in scope -- all it does is visit
instructions, update a map of simplified values, and return whether it
is able to optimize away a particular instruction.
The two cost metrics are now returned as an optional struct. When the
optional is left unengaged, there is no information about the unrolled
cost of the loop, when it is engaged the cost metrics are available to
run against the thresholds.
No functionality changed.
llvm-svn: 238033
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problem instead of suggesting doing something that is trivial to do but
incorrect given the current design of the libraries.
llvm-svn: 237994
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simplified model for use simulating each iteration into a separate
helper function that just returns the cache.
Building this cache had nothing to do with the rest of the unroll
analysis and so this removes an unnecessary coupling, etc. It should
also make it easier to think about the concept of providing fast cached
access to basic SCEV models as an orthogonal concept to the overall
unroll simulation.
I'd really like to see this kind of caching logic folded into SCEV
itself, it seems weird for us to provide it at this layer rather than
making repeated queries into SCEV fast all on their own.
No functionality changed.
llvm-svn: 237993
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a single location.
This reduces code duplication a bit and will also pave the way for
a better separation between the visitation algorithm and the unroll
analysis.
No functionality changed.
llvm-svn: 237990
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checking and make the cache faster and smaller.
I had thought that using an APInt here would be useful, but I think
I was just wrong. Notably, we don't have to do any fancy overflow
checking, we can just bound the values as quite small and do the math in
a higher precision integer. I've switched to a signed integer so that
UBSan will even point out if we ever have integer overflow. I've added
various asserts to try to catch things as well and hoisted the overflow
checks so that we just leave the too-large offsets out of the SCEV-GEP
cache. This makes the value in the cache quite a bit smaller which is
probably worthwhile.
No functionality changed here (for trip counts under 1 billion).
llvm-svn: 237209
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Summary:
This patch reimplements heuristic that tries to estimate optimization beneftis
from complete loop unrolling.
In this patch I kept the minimal changes - e.g. I removed code handling
branches and folding compares. That's a promising area, but now there
are too many questions to discuss before we can enable it.
Test Plan: Tests are included in the patch.
Reviewers: hfinkel, chandlerc
Subscribers: llvm-commits
Differential Revision: http://reviews.llvm.org/D8816
llvm-svn: 237156
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Summary:
Runtime unrolling of loops needs to emit an expression to compute the
loop's runtime trip-count. Avoid runtime unrolling if this computation
will be expensive.
Depends on D8993.
Reviewers: atrick
Subscribers: llvm-commits
Differential Revision: http://reviews.llvm.org/D8994
llvm-svn: 234846
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llvm-svn: 232998
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NFC.
llvm-svn: 232944
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Summary:
Now that the DataLayout is a mandatory part of the module, let's start
cleaning the codebase. This patch is a first attempt at doing that.
This patch is not exactly NFC as for instance some places were passing
a nullptr instead of the DataLayout, possibly just because there was a
default value on the DataLayout argument to many functions in the API.
Even though it is not purely NFC, there is no change in the
validation.
I turned as many pointer to DataLayout to references, this helped
figuring out all the places where a nullptr could come up.
I had initially a local version of this patch broken into over 30
independant, commits but some later commit were cleaning the API and
touching part of the code modified in the previous commits, so it
seemed cleaner without the intermediate state.
Test Plan:
Reviewers: echristo
Subscribers: llvm-commits
From: Mehdi Amini <mehdi.amini@apple.com>
llvm-svn: 231740
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loop from vectorization.
Runtime unrolling is an expensive optimization which can bring benefit
only if the loop is hot and iteration number is relatively large enough.
For some loops, we know they are not worth to be runtime unrolled.
The scalar loop from vectorization is one of the cases.
llvm-svn: 231631
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Canonicalize access to function attributes to use the simpler API.
getAttributes().getAttribute(AttributeSet::FunctionIndex, Kind)
=> getFnAttribute(Kind)
getAttributes().hasAttribute(AttributeSet::FunctionIndex, Kind)
=> hasFnAttribute(Kind)
llvm-svn: 229202
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The issues with the new unroll analyzer are more fundamental than code
cleanup, algorithm, or data structure changes. I've sent an email to the
original commit thread with details and a proposal for how to redesign
things. I'm disabling this for now so that we don't spend time
debugging issues with it in its current state.
llvm-svn: 229064
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UnrollAnalyzer.
Now they share a single worklist and have less implicit state between
them. There was no real benefit to separating these two things out.
I'm going to subsequently refactor things to share even more code.
llvm-svn: 229062
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instruction must by definition be instructions.
llvm-svn: 229061
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contained in it each time we try to add it to the worklist, just check
this when pulling it off the worklist. That way we do it at most once
per instruction with the cost of the worklist set we would need to pay
anyways.
llvm-svn: 229060
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vector.
In addition to dramatically reducing the work required for contrived
example loops, this also has to correct some serious latent bugs in the
cost computation. Previously, we might add an instruction onto the
worklist once for every load which it used and was simplified. Then we
would visit it many times and accumulate "savings" each time.
I mean, fortunately this couldn't matter for things like calls with 100s
of operands, but even for binary operators this code seems like it must
be double counting the savings.
I just noticed this by inspection and due to the runtime problems it can
introduce, I don't have any test cases for cases where the cost produced
by this routine is unacceptable.
llvm-svn: 229059
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std::all_of and a lambda. Much cleaner, no functionality
changed.
llvm-svn: 229058
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In the unroll analyzer, it is checking each user to see if that user
will become dead. However, it first checked if that user was missing
from the simplified values map, and then if was also missing from the
dead instructions set. We add everything from the simplified values map
to the dead instructions set, so the first step is completely subsumed
by the second. Moreover, the first step requires *inserting* something
into the simplified value map which isn't what we want at all.
This also replaces a dyn_cast with a cast as an instruction cannot be
used by a non-instruction.
llvm-svn: 229057
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check.
Also hoist this into the enqueue process as it is faster even than
testing the worklist set, we should just directly filter these out much
like we filter out constants and such.
llvm-svn: 229056
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We don't just want to handle duplicate operands within an instruction,
but also duplicates across operands of different instructions. I should
have gone straight to this, but I had convinced myself that it wasn't
going to be necessary briefly. I've come to my senses after chatting
more with Nick, and am now happier here.
llvm-svn: 229054
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unroll analysis into a lambda and call it. That's much simpler than
duplicating all the code.
llvm-svn: 229053
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into the worklist. This avoids allocating lots of worklist memory for
them when there are large numbers of repeated operands.
llvm-svn: 229052
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reasonably quickly.
I don't have a reduced test case, but for a version of FFMPEG, this
makes the loop unroller start finishing at all (after over 15 minutes of
running, it hadn't terminated for me, no idea if it was a true infloop
or just exponential work).
The key thing here is to check the DeadInstructions set when pulling
things off the worklist. Without this, we would re-walk the user list of
already dead instructions again and again and again. Consider phi nodes
with many, many operands and other patterns.
The other important aspect of this is that because we would keep
re-visiting instructions that were already known dead, we kept adding
their cost savings to this! This would cause our cost savings to be
*insanely* inflated from this.
While I was here, I also rotated the operand walk out of the worklist
loop to make the code easier to read. There is still work to be done to
minimize worklist traffic because we don't de-duplicate operands. This
means we may add the same instruction onto the worklist 1000s of times
if it shows up in 1000s of operansd to a PHI node for example.
Still, with this patch, the ffmpeg testcase I have finishes quickly and
I can't measure the runtime impact of the unroll analysis any more. I'll
probably try to do a few more cleanups to this code, but not sure how
much cleanup I can justify right now.
llvm-svn: 229038
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readable.
The biggest thing that was causing me problems is recognizing the
references vs. poniters here. I also found that for maps naming the loop
variable as KeyValue helps make it obvious why you don't actually use it
directly. Finally, using 'auto' instead of 'User *' doesn't seem like
a good tradeoff. Much like with the other cases, I like to know its
a pointer, and 'User' is just as long and tells the reader a lot more.
llvm-svn: 229033
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hard to type and read for me, and is inconsistent with the other
abbreviation in the base class "Inst". For most of these (where they are
used widely) I prefer just spelling it out as Instruction. I've changed
two of the short-lived variables to use "Inst" to match the base class.
llvm-svn: 229028
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instructions optimized. NFC, just separating this out from the
functionality changing commit.
llvm-svn: 229026
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This is much more efficient. In particular, the query with the user
instruction has to insert a false for every missing instruction into the
set. This is just a cleanup a long the way to fixing the underlying
algorithm problems here.
llvm-svn: 228994
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When we try to estimate number of potentially removed instructions in
loop unroller, we analyze first N iterations and then scale the
computed number by TripCount/N. We should bail out early if N is 0.
llvm-svn: 228988
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conventions for function names consistently. Some were already using
this but not all.
llvm-svn: 228987
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If complete-unroll could help us to optimize away N% of instructions, we
might want to do this even if the final size would exceed loop-unroll
threshold. However, we don't want to unroll huge loop, and we are add
AbsoluteThreshold to avoid that - this threshold will never be crossed,
even if we expect to optimize 99% instructions after that.
llvm-svn: 228434
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It is a variation of SimplifyBinOp, but it takes into account
FastMathFlags.
It is needed in inliner and loop-unroller to accurately predict the
transformation's outcome (previously we dropped the flags and were too
conservative in some cases).
Example:
float foo(float *a, float b) {
float r;
if (a[1] * b)
r = /* a lot of expensive computations */;
else
r = 1;
return r;
}
float boo(float *a) {
return foo(a, 0.0);
}
Without this patch, we don't inline 'foo' into 'boo'.
llvm-svn: 228432
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Complete loop unrolling can make some loads constant, thus enabling a
lot of other optimizations. To catch such cases, we look for loads that
might become constants and estimate number of instructions that would be
simplified or become dead after substitution.
Example:
Suppose we have:
int a[] = {0, 1, 0};
v = 0;
for (i = 0; i < 3; i ++)
v += b[i]*a[i];
If we completely unroll the loop, we would get:
v = b[0]*a[0] + b[1]*a[1] + b[2]*a[2]
Which then will be simplified to:
v = b[0]* 0 + b[1]* 1 + b[2]* 0
And finally:
v = b[1]
llvm-svn: 228265
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Summary: MSVC can compile "LoopID->getOperand(0) == LoopID" when LoopID is MDNode*.
Test Plan: no regression
Reviewers: mkuper
Subscribers: jholewinski, llvm-commits
Differential Revision: http://reviews.llvm.org/D7327
llvm-svn: 227853
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per-function and supports the exact desired interface.
llvm-svn: 227743
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getTTI method used to get an actual TTI object.
No functionality changed. This just threads the argument and ensures
code like the inliner can correctly look up the callee's TTI rather than
using a fixed one.
The next change will use this to implement per-function subtarget usage
by TTI. The changes after that should eliminate the need for FTTI as that
will have become the default.
llvm-svn: 227730
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Summary:
CUDA driver can unroll loops when jit-compiling PTX. To prevent CUDA
driver from unrolling a loop marked with llvm.loop.unroll.disable is not
unrolled by CUDA driver, we need to emit .pragma "nounroll" at the
header of that loop.
This patch also extracts getting unroll metadata from loop ID metadata
into a shared helper function.
Test Plan: test/CodeGen/NVPTX/nounroll.ll
Reviewers: eliben, meheff, jholewinski
Reviewed By: jholewinski
Subscribers: jholewinski, llvm-commits
Differential Revision: http://reviews.llvm.org/D7041
llvm-svn: 227703
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type erased interface and a single analysis pass rather than an
extremely complex analysis group.
The end result is that the TTI analysis can contain a type erased
implementation that supports the polymorphic TTI interface. We can build
one from a target-specific implementation or from a dummy one in the IR.
I've also factored all of the code into "mix-in"-able base classes,
including CRTP base classes to facilitate calling back up to the most
specialized form when delegating horizontally across the surface. These
aren't as clean as I would like and I'm planning to work on cleaning
some of this up, but I wanted to start by putting into the right form.
There are a number of reasons for this change, and this particular
design. The first and foremost reason is that an analysis group is
complete overkill, and the chaining delegation strategy was so opaque,
confusing, and high overhead that TTI was suffering greatly for it.
Several of the TTI functions had failed to be implemented in all places
because of the chaining-based delegation making there be no checking of
this. A few other functions were implemented with incorrect delegation.
The message to me was very clear working on this -- the delegation and
analysis group structure was too confusing to be useful here.
The other reason of course is that this is *much* more natural fit for
the new pass manager. This will lay the ground work for a type-erased
per-function info object that can look up the correct subtarget and even
cache it.
Yet another benefit is that this will significantly simplify the
interaction of the pass managers and the TargetMachine. See the future
work below.
The downside of this change is that it is very, very verbose. I'm going
to work to improve that, but it is somewhat an implementation necessity
in C++ to do type erasure. =/ I discussed this design really extensively
with Eric and Hal prior to going down this path, and afterward showed
them the result. No one was really thrilled with it, but there doesn't
seem to be a substantially better alternative. Using a base class and
virtual method dispatch would make the code much shorter, but as
discussed in the update to the programmer's manual and elsewhere,
a polymorphic interface feels like the more principled approach even if
this is perhaps the least compelling example of it. ;]
Ultimately, there is still a lot more to be done here, but this was the
huge chunk that I couldn't really split things out of because this was
the interface change to TTI. I've tried to minimize all the other parts
of this. The follow up work should include at least:
1) Improving the TargetMachine interface by having it directly return
a TTI object. Because we have a non-pass object with value semantics
and an internal type erasure mechanism, we can narrow the interface
of the TargetMachine to *just* do what we need: build and return
a TTI object that we can then insert into the pass pipeline.
2) Make the TTI object be fully specialized for a particular function.
This will include splitting off a minimal form of it which is
sufficient for the inliner and the old pass manager.
3) Add a new pass manager analysis which produces TTI objects from the
target machine for each function. This may actually be done as part
of #2 in order to use the new analysis to implement #2.
4) Work on narrowing the API between TTI and the targets so that it is
easier to understand and less verbose to type erase.
5) Work on narrowing the API between TTI and its clients so that it is
easier to understand and less verbose to forward.
6) Try to improve the CRTP-based delegation. I feel like this code is
just a bit messy and exacerbating the complexity of implementing
the TTI in each target.
Many thanks to Eric and Hal for their help here. I ended up blocked on
this somewhat more abruptly than I expected, and so I appreciate getting
it sorted out very quickly.
Differential Revision: http://reviews.llvm.org/D7293
llvm-svn: 227669
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a LoopInfoWrapperPass to wire the object up to the legacy pass manager.
This switches all the clients of LoopInfo over and paves the way to port
LoopInfo to the new pass manager. No functionality change is intended
with this iteration.
llvm-svn: 226373
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When we compute the size of a loop, we include the branch on the backedge and
the comparison feeding the conditional branch. Under normal circumstances,
these don't get replicated with the rest of the loop body when we unroll. This
led to the somewhat surprising behavior that really small loops would not get
unrolled enough -- they could be unrolled more and the resulting loop would be
below the threshold, because we were assuming they'd take
(LoopSize * UnrollingFactor) instructions after unrolling, instead of
(((LoopSize-2) * UnrollingFactor)+2) instructions. This fixes that computation.
llvm-svn: 225565
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a cache of assumptions for a single function, and an immutable pass that
manages those caches.
The motivation for this change is two fold. Immutable analyses are
really hacks around the current pass manager design and don't exist in
the new design. This is usually OK, but it requires that the core logic
of an immutable pass be reasonably partitioned off from the pass logic.
This change does precisely that. As a consequence it also paves the way
for the *many* utility functions that deal in the assumptions to live in
both pass manager worlds by creating an separate non-pass object with
its own independent API that they all rely on. Now, the only bits of the
system that deal with the actual pass mechanics are those that actually
need to deal with the pass mechanics.
Once this separation is made, several simplifications become pretty
obvious in the assumption cache itself. Rather than using a set and
callback value handles, it can just be a vector of weak value handles.
The callers can easily skip the handles that are null, and eventually we
can wrap all of this up behind a filter iterator.
For now, this adds boiler plate to the various passes, but this kind of
boiler plate will end up making it possible to port these passes to the
new pass manager, and so it will end up factored away pretty reasonably.
llvm-svn: 225131
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