[llvm][LV] Replace `unsigned VF` with `ElementCount VF` [NFCI]

[fpetrogalli]

Summary

Changes:

• Change ToVectorTy to deal directly with ElementCount instances.
• VF == 1 replaced with VF.isScalar().
• VF > 1 and VF >=2 replaced with VF.isVector().
• VF <=1 is replaced with VF.isZero() || VF.isScalar().
• Replaced the uses of llvm::SmallSet<ElementCount, ...> with llvm::SmallSetVector<ElementCount, ...>. This avoids the need of an ordering function for the ElementCount class.
• Bits and pieces around printing the ElementCount to string streams.

To guarantee that this change is a NFC, VF.Min and asserts are used
in the following places:

1. When it doesn't make sense to deal with the scalable property, for

example:

a. When computing unrolling factors.
b. When shuffle masks are built for fixed width vector types

In this cases, an
assert(!VF.Scalable && "<mgs>") has been added to make sure we don't
enter coepaths that don't make sense for scalable vectors.

2. When there is a conscious decision to use FixedVectorType. These

uses of FixedVectorType will likely be removed in favour of
VectorType once the vectorizer is generic enough to deal with both
fixed vector types and scalable vector types.

3. When dealing with building constants out of the value of VF, for

example when computing the vectorization step, or building vectors
of indices. These operation _make sense_ for scalable vectors too,
but changing the code in these places to be generic and make it work
for scalable vectors is to be submitted in a separate patch, as it is
a functional change.

4. When building the potential VFs in VPlan. Making the VPlan generic

enough to handle scalable vectorization factors is a functional change
that needs a separate patch. See for example `void
LoopVectorizationPlanner::buildVPlans(unsigned MinVF, unsigned
MaxVF)`.

5. The class IntrinsicCostAttribute: this class still uses `unsigned

VF` as updating the field to use ElementCount woudl require changes
that could result in changing the behavior of the compiler. Will be done
in a separate patch.

7. When dealing with user input for forcing the vectorization

factor. In this case, adding support for scalable vectorization is a
functional change that migh require changes at command line.

Note that in some places the idiom

unsigned VF = ...
auto VTy = FixedVectorType::get(ScalarTy, VF)

has been replaced with

ElementCount VF = ...
assert(!VF.Scalable && ...);
auto VTy = VectorType::get(ScalarTy, VF)

The assertion guarantees that the new code is (at least in debug mode)
functionally equivalent to the old version. Notice that this change had been
possible because none of the methods that are specific to FixedVectorType
were used after the instantiation of VTy.

[rogfer01]

Hi @fpetrogalli, thanks for this proposal.

We had to do similar things when we applied the LoopVectorizer to RISC-V V-ext. However we didn't use ElementCount so our version looks much less neat than your approach.

I think this direction makes a lot of sense.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

1256

Can we use VF.isScalar() here as well or I am missing something?

[rengolin]

I think this is a good direction, too.

I don't like implicit assumptions or magic constants, especially when the two (bool scalable and int min) are not linearly independent.

The fact that the patch is huge doesn't make much difference when it's mostly mechanical.

There may be some leftovers, so I suggest you do a grep for Min > and variations, to make sure you got all of them.

I have some comments inline but am mostly happy with it.

llvm/include/llvm/Support/TypeSize.h

82

Why?

101

Should we have a getScalable(int Min), getNonScalable(int Min) and getDisabled() too?

There are a number of places you changed from VF to {VF, false} and places you use literal {0, false} that would benefit from having proper names as the static builders. Using them everywhere would make the code safer and clearer to read.

llvm/lib/Transforms/Vectorize/LoopVectorizationPlanner.h

232–235

Here, a getDisabled() would make the code clearer...

[fpetrogalli]

In D85794#2212309, @rogfer01 wrote:

We had to do similar things when we applied the LoopVectorizer to RISC-V V-ext. However we didn't use ElementCount so our version looks much less neat than your approach.

Out of curiosity, what is your approach? Carrying around a bool Scalable flag?

I think this direction makes a lot of sense.

Thanks!

llvm/include/llvm/Support/TypeSize.h

82

That's just a leftover. Removed.

101

Should we have a getScalable(int Min), getNonScalable(int Min) and getDisabled() too?

I'd rather not, for the following reasons. Please let me know if you disagree.

1. getScalable(int Min), getNonScalable(int Min) for replacing uses when using {VF, false}

The instances of {VF, false} are there just because I want to keep this a non-functional change. Those uses are mostly in places were VPlan is computing the possible ElementCount to evaluate. Once we extend VPlan to add also the scalable ECs, we will (likely) just add an extra level of loop iteration on the two values false/true of scalable, and use the ElementCount constructor directly. Once that is done, the two static methods you propose will become obsolete.

I agree that looking at the code with {VF, false} may be confusing. Would you prefer me to explicitly call the constructor instead of using an initializer list, so that we know we are dealing with ElementCount?

So, I see two approaches, let me know which one is the best in your opinion:

a. call the constructor explicitly instead of the initializer list
b. have a static method getFixed or getNonScalable (I prefer the former) that we use but mark it as deprecated as we shouldn't really be using it once the vectorizer doesn't care anymore about scalable and non scalable ECs.

2. getDisabled

I am happy to add a method for the value of 'zero", but calling it getDisabled is misleading because ther eis no concept of Enabled/Disabled in ElementCount. I'd prefer to call it getZero. I am going this route for this change, let me know if you disagree, will figure something out.

llvm/lib/Transforms/Vectorize/LoopVectorizationPlanner.h

232–235

I used getZero, with a comment saying that 0 EC means vectorization is disabled.

[fpetrogalli]

In D85794#2212993, @rengolin wrote:

I think this is a good direction, too.

I don't like implicit assumptions or magic constants, especially when the two (bool scalable and int min) are not linearly independent.

As mentioned in one of the inline replies, I'd rather avoid creating getScalable and getNonScalable methods in ElementCount. I have explained why, and proposed two alternative approaches. Let me know what you think.

There may be some leftovers, so I suggest you do a grep for Min > and variations, to make sure you got all of them.

On it! Thank you for pointing this out.

I have some comments inline but am mostly happy with it.

Thanks!

[fpetrogalli]

Thank you @rengolin and @rogfer01 for the review.

I have applied all feedback, but I have proposed an alternative approach to the one suggested by @rengolin in one of the comments, for static methods like ElementCount::get[Non]Scalable.

It might be worth if you folks also reply to the RFC in the maling list to say that you are happy with this proposal.

[rogfer01]

I have applied all feedback, but I have proposed an alternative approach to the one suggested by @rengolin in one of the comments, for static methods like ElementCount::get[Non]Scalable.

If the suggested methods will become unused once we start iterating over the scalable component, then perhaps we can live with those {VF, false} things. Calling the constructor makes more explicit what is being constructed so I think it is better than just the initializer-list (at expense of some extra verbosity).

ElementCount::getDisabled does not seem ideal as it is unclear what a "disabled" ElementCount means. ElementCount::getZero looks to me slightly better as long we stay within the domain of "counting things".

[rogfer01]

Out of curiosity, what is your approach? Carrying around a bool Scalable flag?

We keep some state around that tells us if we're in a scalable mode. But this doesn't work if we want to be able to vectorize both for fixed size or scalable at the same time for the same target. Not sure if you're considering this scenario (e.g. Advanced SIMD + SVE-256)

[vkmr]

Thanks @fpetrogalli for this proposal! I agree with @rogfer01 and @rengolin in that this approach makes a lot of sense.
I have added a few comments inline. Overall looks good.

llvm/include/llvm/Support/TypeSize.h

96

Unless there is something explicitly preventing from constructing a {0, true} ElementCount, this could potentially hide bugs by returning true for this case. Constraining ElementCount constructor is probably a better way.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

2735–2737

Typo (me -> make)?

6233

Missing message for assert.
I believe this assert falls under point 4 (When building the potential VFs in VPlan. Making the VPlan generic) in your description of where it would be a functional change to support scalable vectors, right?

6264–6266

Missing message in assert.

[fpetrogalli]

llvm/include/llvm/Support/TypeSize.h

96

Ah - good point! I have added an assertion in the constructor and made the condition here more strict to that {0, true} is not considered a vector.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

2735–2737

Yep, thank you. I have also added an assert as this is a good place where we have to make a conscious choice for enabling scalable vectorization

6233

Yes, you are correct. I have added this message:

assert(!VF.Scalable && "the cost model is not yet implememented for scalable vectorization");

[fpetrogalli]

Thank you for the reviews.

@vkmr, I have addressed your comments.

I have replaced the initializer lists used for bnuilding ECs instances
with the explicit constructor. The reasoning that led to this makes me
thing that maybe we shoudl make Min and scalable private in the
ElementCount class... but that definitely for a separate patch.

[fpetrogalli]

In D85794#2213912, @rogfer01 wrote:

Out of curiosity, what is your approach? Carrying around a bool Scalable flag?

We keep some state around that tells us if we're in a scalable mode. But this doesn't work if we want to be able to vectorize both for fixed size or scalable at the same time for the same target. Not sure if you're considering this scenario (e.g. Advanced SIMD + SVE-256)

I don't see why we should exclude this scenario. Better to keep the possibilities open for now.

[rengolin]

llvm/include/llvm/Support/TypeSize.h

101

Once we extend VPlan to add also the scalable ECs, we will (likely) just add an extra level of loop iteration on the two values false/true of scalable, and use the ElementCount constructor directly. Once that is done, the two static methods you propose will become obsolete.

I see what you mean, makes sense.

I agree that looking at the code with {VF, false} may be confusing. Would you prefer me to explicitly call the constructor instead of using an initializer list, so that we know we are dealing with ElementCount?

No, init-list constants are usually more readable than explicit constructor calls. The point was not init-list vs c-tor, but constant vs named call, which won't work long term, as you explained above.

2. getDisabled

I am happy to add a method for the value of 'zero", but calling it getDisabled is misleading because ther eis no concept of Enabled/Disabled in ElementCount. I'd prefer to call it getZero. I am going this route for this change, let me know if you disagree, will figure something out.

LGTM. Thanks!

[fpetrogalli]

llvm/include/llvm/Support/TypeSize.h

101

No, init-list constants are usually more readable than explicit constructor calls.

In the last update of the patch (before your comment) I have replaced the init-list with explicit constructor calls. I just want to make sure you are happy with this. I am am fan of init-list in general, but in this specific case I prefer the constructor, as it make it explicit that we are building something that is used to count the number of elements.

[ctetreau]

llvm/include/llvm/Support/TypeSize.h

72

I'm uncomfortable with having this comparitor. is {true, 4} > {false, 4}? Depends on the machine. I would personally prefer a named function that can be passed to the comparator parameter of containers. I understand that this makes the code uglier, so if you get significant pushback it may be worth caving on this issue.

96

Constraining the constructor doesn't help with the case where a valid ElementCount is constructed, and then Min set to zero later on.

Why exactly is {true, 0} forbidden? forall N, N * 0 = 0 after all.

[ctetreau]

@fpetrogalli I haven't fully reviewed it yet, but assuming it does what it says on the tin I think this is a great approach.

[fpetrogalli]

llvm/include/llvm/Support/TypeSize.h

72

I think what you say makes sense. I had to introduce a comparison operator because llvm::SmallSet is being used in VPlan.

An alternative approach would be to remove the small set and have a hash function that could be used for std::unordered_set. I actually prefer this latter approach as it avoid introducing the concept of ordering ElementCount, which is kind of formally wrong as you have pointed out.

96

Mathematically you are right. ElementCount is a monomial of grade 0 (a*X^0) or 1 (a*X^1). It doesn't really matter if you are multiplying by 0*X^0 or 0*X^1, both values will null the result of the operation independently of the EC in input.

I think that @vkmr was mostly concerned about potential bugs that can happen when we check if we are working in scalable mode (so Scalable == true) but we forget to check that we have actually a non zero value by checking Min > 0. It is very unlikely to happen, but I guess there is some argument for choosing this approach.

All in all, I'd rather avoid {0, true}, at list in debug mode, just to catch things that might end up weird if we end up producing this value.

[fpetrogalli]

llvm/include/llvm/Support/TypeSize.h

72

Ah, llvm::SmallSetVector! I think I am going to try that first, so we don't need both ordering function and the STL.

[fpetrogalli]

I have removed the ordering function of ElementCount. It required replacing the uses of llvm::SmallSet<ElementCount,...> with llvm::SmallSetVector<ElementCount, ...>.

Commit message has been updated accordingly.

[vkmr]

llvm/include/llvm/Support/TypeSize.h

96

@ctetreau I see your point about constraining the constructor for scalable vectors; ElementCount is a counter and 0 is a valid count. Also, however one defines a "vector with 0 elements", it would be the same for fixed and scalar vectors. So, IF the constructor allows constructing fixed vectors of 0 length, it should also allow scalable vectors of 0 length.

The perspective behind not allowing to have Min = 0 (for both fixed and scalable vectors) is that ElementCount's only purpose is to count the number of elements in a vector, and a vector with no elements is illegal vector. But that discussion would be out of scope of this patch.

The updated condition for isVector() still makes sense though.

[ctetreau]

I understand that this is WIP, but I went ahead and pointed out several missing asserts. I assume the real patch will be based on this at least to some extent.

llvm/include/llvm/Support/TypeSize.h

72

Honestly, I think it's reasonable to want to put an ElementCount in an ordered map or set. If it's just one place, it might be an easier sell to use an explicit named comparator.

I think it's really unfortunate that operator< is overloaded in this way in C++.

96

So you're telling me that there's no good reason to construct a {0, true}, so as a debugging aid you're asserting that it doesn't happen? Why is {0, false} OK? I could see some valid code doing something like:

ElementCount EC = {std::min(someQuantity, someOtherQuantity), true};
if (!EC.isVector())
   return false;

Sure, I could delay construction of the ElementCount, but there's no good reason that I should have to do that.

Really though, as long as the Min and Scalable fields are public, I don't think we should try to preserve any invariants for them. They should either be made private (probably outside the scope of what you are trying to do), or its functionality should be expected to work for any combination of {Min, Scalable}.

101

My thinking is that people shouldn't be manually constructing ElementCount objects very often. FixedVectorType and ScalableVectorType both have static getters that construct instances using only a number, and vectors have a method to get an ElementCount. I would think code that generically produces vectors that may either be fixed width or scalable from the ground up would not be common.

As for having a deprecated getFixed(), I'd like to point out that we have build bots that build with -werror, so deprecating methods with the intention of using them in upstream isn't going to work out.

104

I don't think we should add a getScalar and getZero, because it would be easy to assume these work for scalable vectors end up writing a bunch of bad code. Sure it's an easy fix, but best to just avoid the opportunity for misuse.

llvm/lib/Transforms/Vectorize/LoopVectorizationPlanner.h

235

perhaps this should just be an llvm::Optional<ElementCount>?

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

326

this can just become VectorType::get(Ty, VF)

879–880

should assert that Scalable is false

1829

IRBuilder has a CreateVectorSplat that takes an ElementCount

2147

assert not scalable

2294–2295

assert not scalable

2494

assert not scalable

2829

assert not scalable

3285

assert not scalable

3752

assert not scalable

3993–3994

assert not scalable

5525–5526

assert not scalable

5712–5713

assert not scalable

5941

assert not scalable

[ctetreau]

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

6185–6186

assert not scalable

6484–6488

assert not scalable

6712–6713

assert not scalable

llvm/lib/Transforms/Vectorize/VPlan.cpp

303–304

assert not scalable

llvm/lib/Transforms/Vectorize/VPlan.h

154–156

NIT: the message doesn't match the VF being scalable. Should be split out

[vkmr]

llvm/include/llvm/Support/TypeSize.h

96

@fpetrogalli, I saw your reply after writing my comment.
My main concern was just the condition in isVector(), which in the original draft was return (Scalable || Min > 1;, that would incorrectly report {0, true} to be a vector, (but not {0, false}.
As I mentioned in the other comment, for this patch I would prefer a consistent behavior for fixed and scalable vectors where it makes sense. We can remove the asserts for "invalid values: zero can not be scalable". Also isZero() works for both, so I am not too worried.

[ctetreau]

llvm/include/llvm/Support/TypeSize.h

101

Thinking on this, I kind of like having two static functions ElementCount::Fixed(unsigned) and ElementCount::Scalable(unsigned), and hiding all other constructors. This will ensure that, in a hypothetical future where some third kind of vector is added, that code that thinks it's correctly constructing a fixed width ElementCount actually is.

My previous comment stands, for the most part, only instances of VectorType should be constructing ElementCounts manually. But if some other code needs to, we should make it easy to do right.

[fpetrogalli]

Thank you again for the review. In the last update I have:

1. Removed the methods getScalar and getZero of

ElementCount. The constructor is not invoked directly when needed. I
agree with @ctetreau that ideally we should hide all constructor of
ElementCount in terms of using static method getFixed and
getScalable, but I would like to do this in a separate
patch. perhapd, this could be done as a follow up patch to the changes
that @david-arm is implementing in making the members ElementCount
private (see D86065).

2. I have added all the missing assertions. Thank you for pointing them out, @ctetreau.

3. I have converted some of the FixedVectorType::get into

VectorType::get. To guarantee that the code is still an NFC, I have
added an assert that checks that the ElementCount instance being
sued to construct the vector type is not scalable.

4. I have updated the description of the differential review to

address for these changes.

4. BestVF in VPlan is now of type Optional<ElementCount> so that

we don't need to construc any ElementCount instance with Min = 0.

llvm/include/llvm/Support/TypeSize.h

96

I have removed the constrain in the constructor.

101

I see your point here. If it is OK for you (please confirm), I will create a separate patch that add these two static methods and remove the explicit constructor. For this patch, I have (temporarily) gone for using explicit constructors.

104

I see your point. I have removed getZero and getScalar.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

2294–2295

Done, I have also replaced FixedVectorType with VectorType.

[ctetreau]

What you have so far seems like a good approach. I'll refain from giving approval as this is still WIP and other people should give it a look. It might be easier to get it in after D86065 and any follow patches to that land anyways.

llvm/include/llvm/Support/TypeSize.h

101

That's fine. It probably makes sense to do this either in D86065 or as a follow up to that patch. The way I see it, this work is about "using ElementCount" and David's is about "improving ElementCount"

[fpetrogalli]

@ctetreau - I had made the constructor of ElementCount private in https://reviews.llvm.org/D86120.

[fpetrogalli]

I have updated the changes on top of https://reviews.llvm.org/D86120 (no constructors for ElementCount).

[ctetreau]

I put a comment on most of them, but just to elaborate: for any assert of the form assert(!SomeElementCount.Scalable && "invalid number of elements");, the issue is that "invalid number of elements" could mean literally anything (is it invalid because there are 4 elements? Who knows). Obviously if you look at the expression being asserted, it narrows it down to just scalable = true being invalid. But it doesn't answer the question "why is it invalid?". Is it actually nonsensical? Is it just not implemented? I may have missed some, but if I did it was an oversight.

Overall, this patch is almost there. We just need to get the bike shed painted the correct color. :)

llvm/include/llvm/Support/TypeSize.h

88

since we allow zero-element scalable element counts now, this assert should be removed

llvm/lib/Transforms/Vectorize/LoopVectorizationPlanner.h

181

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

232–235

update this comment

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

322

The assert message is unclear. Change it to something like "scalable vectors not yet supported".

If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

842

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

879–880

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

1196

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

1308

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

1326

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

1332–1334

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

1825

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

1962–1964

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2146–2148

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2294–2295

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2349–2350

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2367

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2397–2398

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2449

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2494

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

2827

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

3284–3285

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

3419

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

3993–3994

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

4318

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

4463

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

4852

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

5341

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

5525–5526

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

5712–5713

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

5940–5942

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

5950

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

5988

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

6071

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

6185–6186

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

6296

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

6482

The assert message is unclear. Change it to something like "scalable vectors not yet supported". If this function does not make sense for scalable vectors, then we should explicitly operate on FixedVectorType

6624

NIT: remove /*Min*/, it's unambiguous what this 1 is here.

6957

For BestVF.getValue(), I would use Optional<T>::operator*

[fpetrogalli]

Thank you for the review, @ctetreau.

Francesco

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

322

I think that this function makes sense also for scalable vectors.

[ctetreau]

Fix that one straggler, then this LGTM.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

1196

looks like you missed this one

[fpetrogalli]

I fixed one assertion message that I missed in last patch update.

Thank you for the approval @ctetreau.

Francesco

[rengolin]

I'm a bit wary of all the new asserts. The vectoriser is reasonable self-contained, and if you check for the scalable flag at entry points, you can assume safety inside.

But I'm not wary enough to go through the list of all possible entry-points. I think if the plan is to start implementing those pieces right away, then it could be fine as it for the time being.

I only have two small comments and LGTM. Thanks!

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

6297

unnecessary test on !VF.Scalable already checked by assert.

6838

Name overloading, but now with different types. this will cause confusion.

Better to have ElementCount VF = UserVF and then use VF.Min locally.

[fpetrogalli]

Thank you @rengolin, I have addressed your feedback.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

6838

Good point, done. This allowed some extra improvements like replacing !UserVF.Min with UserVF.isZero().

llvm/include/llvm/Analysis/TargetTransformInfo.h

 
 public:
   IntrinsicCostAttributes(const IntrinsicInst &I);
 
   IntrinsicCostAttributes(Intrinsic::ID Id, const CallBase &CI);
 
   IntrinsicCostAttributes(Intrinsic::ID Id, const CallBase &CI,
                           unsigned Factor);
+  IntrinsicCostAttributes(Intrinsic::ID Id, const CallBase &CI,
+                          ElementCount Factor)
+      : IntrinsicCostAttributes(Id, CI, Factor.Min) {
+    assert(!Factor.Scalable);
+  }
 
   IntrinsicCostAttributes(Intrinsic::ID Id, const CallBase &CI,
                           unsigned Factor, unsigned ScalarCost);
 
   IntrinsicCostAttributes(Intrinsic::ID Id, Type *RTy,
                           ArrayRef<Type *> Tys, FastMathFlags Flags);
 
   IntrinsicCostAttributes(Intrinsic::ID Id, Type *RTy,

llvm/include/llvm/Analysis/VectorUtils.h

 class TargetTransformInfo;
 class Type;
 class Value;
 
 namespace Intrinsic {
 typedef unsigned ID;
 }
 
-/// A helper function for converting Scalar types to vector types.
-/// If the incoming type is void, we return void. If the VF is 1, we return
-/// the scalar type.
-inline Type *ToVectorTy(Type *Scalar, unsigned VF, bool isScalable = false) {
-  if (Scalar->isVoidTy() || VF == 1)
+/// A helper function for converting Scalar types to vector types. If
+/// the incoming type is void, we return void. If the EC represents a
+/// scalar, we return the scalar type.
+inline Type *ToVectorTy(Type *Scalar, ElementCount EC) {
+  if (Scalar->isVoidTy() || EC.isScalar())
     return Scalar;
-  return VectorType::get(Scalar, ElementCount::get(VF, isScalable));
+  return VectorType::get(Scalar, EC);
+}
+
+inline Type *ToVectorTy(Type *Scalar, unsigned VF) {
+  return ToVectorTy(Scalar, ElementCount::getFixed(VF));
 }
 
 /// Identify if the intrinsic is trivially vectorizable.
 /// This method returns true if the intrinsic's argument types are all scalars
 /// for the scalar form of the intrinsic and all vectors (or scalars handled by
 /// hasVectorInstrinsicScalarOpd) for the vector form of the intrinsic.
 bool isTriviallyVectorizable(Intrinsic::ID ID);
 

llvm/include/llvm/IR/DiagnosticInfo.h

 
 #include "llvm-c/Types.h"
 #include "llvm/ADT/Optional.h"
 #include "llvm/ADT/SmallVector.h"
 #include "llvm/ADT/StringRef.h"
 #include "llvm/ADT/Twine.h"
 #include "llvm/IR/DebugLoc.h"
 #include "llvm/Support/CBindingWrapping.h"
+#include "llvm/Support/TypeSize.h"
 #include "llvm/Support/YAMLTraits.h"
 #include <algorithm>
 #include <cstdint>
 #include <functional>
 #include <iterator>
 #include <string>
 
 namespace llvm {
struct Argument {
     Argument(StringRef Key, const char *S) : Argument(Key, StringRef(S)) {};
     Argument(StringRef Key, int N);
     Argument(StringRef Key, float N);
     Argument(StringRef Key, long N);
     Argument(StringRef Key, long long N);
     Argument(StringRef Key, unsigned N);
     Argument(StringRef Key, unsigned long N);
     Argument(StringRef Key, unsigned long long N);
+    Argument(StringRef Key, ElementCount EC);
     Argument(StringRef Key, bool B) : Key(Key), Val(B ? "true" : "false") {}
     Argument(StringRef Key, DebugLoc dl);
   };
 
   /// \p PassName is the name of the pass emitting this diagnostic. \p
   /// RemarkName is a textual identifier for the remark (single-word,
   /// camel-case). \p Fn is the function where the diagnostic is being emitted.
   /// \p Loc is the location information to use in the diagnostic. If line table

llvm/include/llvm/Support/TypeSize.h

ElementCount NextPowerOf2() const {
     return {(unsigned)llvm::NextPowerOf2(Min), Scalable};
   }
 
   static ElementCount getFixed(unsigned Min) { return {Min, false}; }
   static ElementCount getScalable(unsigned Min) { return {Min, true}; }
   static ElementCount get(unsigned Min, bool Scalable) {
     return {Min, Scalable};
   }
+
+  /// Printing function.
+  void print(raw_ostream &OS) const {
+    if (Scalable)
+      OS << "vscale x ";
+    OS << Min;
+  }
+  /// Counting predicates.
+  ///
+  /// Notice that Min = 1 and Scalable = true is considered more than
+  /// one element.
+  ///
+  ///@{ No elements..
+  bool isZero() const { return Min == 0; }
+  /// Exactly one element.
+  bool isScalar() const { return !Scalable && Min == 1; }
+  /// One or more elements.
+  bool isVector() const { return (Scalable && Min != 0) || Min > 1; }
+  ///@}
 };
 
+/// Stream operator function for `ElementCount`.
+inline raw_ostream &operator<<(raw_ostream &OS, const ElementCount &EC) {
+  EC.print(OS);
+  return OS;
+}
+
 // This class is used to represent the size of types. If the type is of fixed
 // size, it will represent the exact size. If the type is a scalable vector,
 // it will represent the known minimum size.
 class TypeSize {
   uint64_t MinSize;   // The known minimum size.
   bool IsScalable;    // If true, then the runtime size is an integer multiple
                       // of MinSize.
 
 public:
   constexpr TypeSize(uint64_t MinSize, bool Scalable)
     : MinSize(MinSize), IsScalable(Scalable) {}
 
   static constexpr TypeSize Fixed(uint64_t Size) {
     return TypeSize(Size, /*IsScalable=*/false);
   }
 

llvm/lib/IR/DiagnosticInfo.cpp

 DiagnosticInfoOptimizationBase::Argument::Argument(StringRef Key,
                                                    unsigned long N)
     : Key(std::string(Key)), Val(utostr(N)) {}
 
 DiagnosticInfoOptimizationBase::Argument::Argument(StringRef Key,
                                                    unsigned long long N)
     : Key(std::string(Key)), Val(utostr(N)) {}
 
+DiagnosticInfoOptimizationBase::Argument::Argument(StringRef Key,
+                                                   ElementCount EC)
+    : Key(std::string(Key)) {
+  raw_string_ostream OS(Val);
+  EC.print(OS);
+}
+
 DiagnosticInfoOptimizationBase::Argument::Argument(StringRef Key, DebugLoc Loc)
     : Key(std::string(Key)), Loc(Loc) {
   if (Loc) {
     Val = (Loc->getFilename() + ":" + Twine(Loc.getLine()) + ":" +
            Twine(Loc.getCol())).str();
   } else {
     Val = "<UNKNOWN LOCATION>";
   }

llvm/lib/Transforms/Vectorize/LoopVectorizationPlanner.h

 /// TODO: The following VectorizationFactor was pulled out of
 /// LoopVectorizationCostModel class. LV also deals with
 /// VectorizerParams::VectorizationFactor and VectorizationCostTy.
 /// We need to streamline them.
 
 /// Information about vectorization costs
 struct VectorizationFactor {
   // Vector width with best cost
-  unsigned Width;
+  ElementCount Width;
   // Cost of the loop with that width
   unsigned Cost;
 
   // Width 1 means no vectorization, cost 0 means uncomputed cost.
-  static VectorizationFactor Disabled() { return {1, 0}; }
+  static VectorizationFactor Disabled() {
+    return {ElementCount::getFixed(1), 0};
+  }
 
   bool operator==(const VectorizationFactor &rhs) const {
     return Width == rhs.Width && Cost == rhs.Cost;
   }
 };
 
 /// Planner drives the vectorization process after having passed
 /// Legality checks.
struct VPCallbackILV : public VPCallback {
     Value *getOrCreateVectorValues(Value *V, unsigned Part) override;
     Value *getOrCreateScalarValue(Value *V,
                                   const VPIteration &Instance) override;
   };
 
   /// A builder used to construct the current plan.
   VPBuilder Builder;
 
-  unsigned BestVF = 0;
+  /// The best number of elements of the vector types used in the
+  /// transformed loop. BestVF = None means that vectorization is
+  /// disabled.
+  Optional<ElementCount> BestVF = None;
   unsigned BestUF = 0;
 
 public:
   LoopVectorizationPlanner(Loop *L, LoopInfo *LI, const TargetLibraryInfo *TLI,
                            const TargetTransformInfo *TTI,
                            LoopVectorizationLegality *Legal,
                            LoopVectorizationCostModel &CM,
                            InterleavedAccessInfo &IAI,
                            PredicatedScalarEvolution &PSE)
       : OrigLoop(L), LI(LI), TLI(TLI), TTI(TTI), Legal(Legal), CM(CM), IAI(IAI),
         PSE(PSE) {}
 
   /// Plan how to best vectorize, return the best VF and its cost, or None if
   /// vectorization and interleaving should be avoided up front.
-  Optional<VectorizationFactor> plan(unsigned UserVF, unsigned UserIC);
+  Optional<VectorizationFactor> plan(ElementCount UserVF, unsigned UserIC);
 
   /// Use the VPlan-native path to plan how to best vectorize, return the best
   /// VF and its cost.
-  VectorizationFactor planInVPlanNativePath(unsigned UserVF);
+  VectorizationFactor planInVPlanNativePath(ElementCount UserVF);
 
   /// Finalize the best decision and dispose of all other VPlans.
-  void setBestPlan(unsigned VF, unsigned UF);
+  void setBestPlan(ElementCount VF, unsigned UF);
 
   /// Generate the IR code for the body of the vectorized loop according to the
   /// best selected VPlan.
   void executePlan(InnerLoopVectorizer &LB, DominatorTree *DT);
 
   void printPlans(raw_ostream &O) {
     for (const auto &Plan : VPlans)
       O << *Plan;
   }
 
   /// Test a \p Predicate on a \p Range of VF's. Return the value of applying
   /// \p Predicate on Range.Start, possibly decreasing Range.End such that the
   /// returned value holds for the entire \p Range.
   static bool
-  getDecisionAndClampRange(const std::function<bool(unsigned)> &Predicate,
+  getDecisionAndClampRange(const std::function<bool(ElementCount)> &Predicate,
                            VFRange &Range);
 
 protected:
   /// Collect the instructions from the original loop that would be trivially
   /// dead in the vectorized loop if generated.
   void collectTriviallyDeadInstructions(
       SmallPtrSetImpl<Instruction *> &DeadInstructions);
 

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

static Type *getMemInstValueType(Value *I) {
   if (auto *LI = dyn_cast<LoadInst>(I))
     return LI->getType();
   return cast<StoreInst>(I)->getValueOperand()->getType();
 }
 
 /// A helper function that returns true if the given type is irregular. The
 /// type is irregular if its allocated size doesn't equal the store size of an
 /// element of the corresponding vector type at the given vectorization factor.
-static bool hasIrregularType(Type *Ty, const DataLayout &DL, unsigned VF) {
+static bool hasIrregularType(Type *Ty, const DataLayout &DL, ElementCount VF) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   // Determine if an array of VF elements of type Ty is "bitcast compatible"
   // with a <VF x Ty> vector.
-  if (VF > 1) {
-    auto *VectorTy = FixedVectorType::get(Ty, VF);
+  if (VF.isVector()) {
+    auto *VectorTy = VectorType::get(Ty, VF);
     return VF * DL.getTypeAllocSize(Ty) != DL.getTypeStoreSize(VectorTy);
   }
 
   // If the vectorization factor is one, we just check if an array of type Ty
   // requires padding between elements.
   return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty);
 }
 

 /// LoopVectorizationLegality class to provide information about the induction
 /// and reduction variables that were found to a given vectorization factor.
 class InnerLoopVectorizer {
 public:
   InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
                       LoopInfo *LI, DominatorTree *DT,
                       const TargetLibraryInfo *TLI,
                       const TargetTransformInfo *TTI, AssumptionCache *AC,
-                      OptimizationRemarkEmitter *ORE, unsigned VecWidth,
+                      OptimizationRemarkEmitter *ORE, ElementCount VecWidth,
                       unsigned UnrollFactor, LoopVectorizationLegality *LVL,
                       LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI,
                       ProfileSummaryInfo *PSI)
       : OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI),
         AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor),
         Builder(PSE.getSE()->getContext()),
         VectorLoopValueMap(UnrollFactor, VecWidth), Legal(LVL), Cost(CM),
         BFI(BFI), PSI(PSI) {
public:
   /// A type for vectorized values in the new loop. Each value from the
   /// original loop, when vectorized, is represented by UF vector values in the
   /// new unrolled loop, where UF is the unroll factor.
   using VectorParts = SmallVector<Value *, 2>;
 
   /// Vectorize a single GetElementPtrInst based on information gathered and
   /// decisions taken during planning.
   void widenGEP(GetElementPtrInst *GEP, VPUser &Indices, unsigned UF,
-                unsigned VF, bool IsPtrLoopInvariant,
+                ElementCount VF, bool IsPtrLoopInvariant,
                 SmallBitVector &IsIndexLoopInvariant, VPTransformState &State);
 
   /// Vectorize a single PHINode in a block. This method handles the induction
   /// variable canonicalization. It supports both VF = 1 for unrolled loops and
   /// arbitrary length vectors.
-  void widenPHIInstruction(Instruction *PN, unsigned UF, unsigned VF);
+  void widenPHIInstruction(Instruction *PN, unsigned UF, ElementCount VF);
 
   /// A helper function to scalarize a single Instruction in the innermost loop.
   /// Generates a sequence of scalar instances for each lane between \p MinLane
   /// and \p MaxLane, times each part between \p MinPart and \p MaxPart,
   /// inclusive. Uses the VPValue operands from \p Operands instead of \p
   /// Instr's operands.
   void scalarizeInstruction(Instruction *Instr, VPUser &Operands,
                             const VPIteration &Instance, bool IfPredicateInstr,
protected:
   /// used.
   ///
   /// This is currently only used to add no-alias metadata based on the
   /// memchecks.  The actually versioning is performed manually.
   std::unique_ptr<LoopVersioning> LVer;
 
   /// The vectorization SIMD factor to use. Each vector will have this many
   /// vector elements.
-  unsigned VF;
+  ElementCount VF;
 
   /// The vectorization unroll factor to use. Each scalar is vectorized to this
   /// many different vector instructions.
   unsigned UF;
 
   /// The builder that we use
   IRBuilder<> Builder;
 
public:
   InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
                     LoopInfo *LI, DominatorTree *DT,
                     const TargetLibraryInfo *TLI,
                     const TargetTransformInfo *TTI, AssumptionCache *AC,
                     OptimizationRemarkEmitter *ORE, unsigned UnrollFactor,
                     LoopVectorizationLegality *LVL,
                     LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI,
                     ProfileSummaryInfo *PSI)
-      : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, 1,
-                            UnrollFactor, LVL, CM, BFI, PSI) {}
+      : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE,
+                            ElementCount::getFixed(1), UnrollFactor, LVL, CM,
+                            BFI, PSI) {}
 
 private:
   Value *getBroadcastInstrs(Value *V) override;
   Value *getStepVector(Value *Val, int StartIdx, Value *Step,
                        Instruction::BinaryOps Opcode =
                        Instruction::BinaryOpsEnd) override;
   Value *reverseVector(Value *Vec) override;
 };
static Instruction *getDebugLocFromInstOrOperands(Instruction *I) {
   return I;
 }
 
 void InnerLoopVectorizer::setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) {
   if (const Instruction *Inst = dyn_cast_or_null<Instruction>(Ptr)) {
     const DILocation *DIL = Inst->getDebugLoc();
     if (DIL && Inst->getFunction()->isDebugInfoForProfiling() &&
         !isa<DbgInfoIntrinsic>(Inst)) {
-      auto NewDIL = DIL->cloneByMultiplyingDuplicationFactor(UF * VF);
+      assert(!VF.Scalable && "scalable vectors not yet supported.");
+      auto NewDIL = DIL->cloneByMultiplyingDuplicationFactor(UF * VF.Min);
       if (NewDIL)
         B.SetCurrentDebugLocation(NewDIL.getValue());
       else
         LLVM_DEBUG(dbgs()
                    << "Failed to create new discriminator: "
                    << DIL->getFilename() << " Line: " << DIL->getLine());
     }
     else
public:
 
   /// \return The most profitable vectorization factor and the cost of that VF.
   /// This method checks every power of two up to MaxVF. If UserVF is not ZERO
   /// then this vectorization factor will be selected if vectorization is
   /// possible.
   VectorizationFactor selectVectorizationFactor(unsigned MaxVF);
 
   /// Setup cost-based decisions for user vectorization factor.
-  void selectUserVectorizationFactor(unsigned UserVF) {
+  void selectUserVectorizationFactor(ElementCount UserVF) {
     collectUniformsAndScalars(UserVF);
     collectInstsToScalarize(UserVF);
   }
 
   /// \return The size (in bits) of the smallest and widest types in the code
   /// that needs to be vectorized. We ignore values that remain scalar such as
   /// 64 bit loop indices.
   std::pair<unsigned, unsigned> getSmallestAndWidestTypes();
 
   /// \return The desired interleave count.
   /// If interleave count has been specified by metadata it will be returned.
   /// Otherwise, the interleave count is computed and returned. VF and LoopCost
   /// are the selected vectorization factor and the cost of the selected VF.
-  unsigned selectInterleaveCount(unsigned VF, unsigned LoopCost);
+  unsigned selectInterleaveCount(ElementCount VF, unsigned LoopCost);
 
   /// Memory access instruction may be vectorized in more than one way.
   /// Form of instruction after vectorization depends on cost.
   /// This function takes cost-based decisions for Load/Store instructions
   /// and collects them in a map. This decisions map is used for building
   /// the lists of loop-uniform and loop-scalar instructions.
   /// The calculated cost is saved with widening decision in order to
   /// avoid redundant calculations.
-  void setCostBasedWideningDecision(unsigned VF);
+  void setCostBasedWideningDecision(ElementCount VF);
 
   /// A struct that represents some properties of the register usage
   /// of a loop.
   struct RegisterUsage {
     /// Holds the number of loop invariant values that are used in the loop.
     /// The key is ClassID of target-provided register class.
     SmallMapVector<unsigned, unsigned, 4> LoopInvariantRegs;
     /// Holds the maximum number of concurrent live intervals in the loop.
     /// The key is ClassID of target-provided register class.
     SmallMapVector<unsigned, unsigned, 4> MaxLocalUsers;
   };
 
   /// \return Returns information about the register usages of the loop for the
   /// given vectorization factors.
-  SmallVector<RegisterUsage, 8> calculateRegisterUsage(ArrayRef<unsigned> VFs);
+  SmallVector<RegisterUsage, 8>
+  calculateRegisterUsage(ArrayRef<ElementCount> VFs);
 
   /// Collect values we want to ignore in the cost model.
   void collectValuesToIgnore();
 
   /// Split reductions into those that happen in the loop, and those that happen
   /// outside. In loop reductions are collected into InLoopReductionChains.
   void collectInLoopReductions();
 
   /// \returns The smallest bitwidth each instruction can be represented with.
   /// The vector equivalents of these instructions should be truncated to this
   /// type.
   const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const {
     return MinBWs;
   }
 
   /// \returns True if it is more profitable to scalarize instruction \p I for
   /// vectorization factor \p VF.
-  bool isProfitableToScalarize(Instruction *I, unsigned VF) const {
-    assert(VF > 1 && "Profitable to scalarize relevant only for VF > 1.");
+  bool isProfitableToScalarize(Instruction *I, ElementCount VF) const {
+    assert(VF.isVector() &&
+           "Profitable to scalarize relevant only for VF > 1.");
 
     // Cost model is not run in the VPlan-native path - return conservative
     // result until this changes.
     if (EnableVPlanNativePath)
       return false;
 
     auto Scalars = InstsToScalarize.find(VF);
     assert(Scalars != InstsToScalarize.end() &&
            "VF not yet analyzed for scalarization profitability");
     return Scalars->second.find(I) != Scalars->second.end();
   }
 
   /// Returns true if \p I is known to be uniform after vectorization.
-  bool isUniformAfterVectorization(Instruction *I, unsigned VF) const {
-    if (VF == 1)
+  bool isUniformAfterVectorization(Instruction *I, ElementCount VF) const {
+    if (VF.isScalar())
       return true;
 
     // Cost model is not run in the VPlan-native path - return conservative
     // result until this changes.
     if (EnableVPlanNativePath)
       return false;
 
     auto UniformsPerVF = Uniforms.find(VF);
     assert(UniformsPerVF != Uniforms.end() &&
            "VF not yet analyzed for uniformity");
     return UniformsPerVF->second.count(I);
   }
 
   /// Returns true if \p I is known to be scalar after vectorization.
-  bool isScalarAfterVectorization(Instruction *I, unsigned VF) const {
-    if (VF == 1)
+  bool isScalarAfterVectorization(Instruction *I, ElementCount VF) const {
+    if (VF.isScalar())
       return true;
 
     // Cost model is not run in the VPlan-native path - return conservative
     // result until this changes.
     if (EnableVPlanNativePath)
       return false;
 
     auto ScalarsPerVF = Scalars.find(VF);
     assert(ScalarsPerVF != Scalars.end() &&
            "Scalar values are not calculated for VF");
     return ScalarsPerVF->second.count(I);
   }
 
   /// \returns True if instruction \p I can be truncated to a smaller bitwidth
   /// for vectorization factor \p VF.
-  bool canTruncateToMinimalBitwidth(Instruction *I, unsigned VF) const {
-    return VF > 1 && MinBWs.find(I) != MinBWs.end() &&
+  bool canTruncateToMinimalBitwidth(Instruction *I, ElementCount VF) const {
+    return VF.isVector() && MinBWs.find(I) != MinBWs.end() &&
            !isProfitableToScalarize(I, VF) &&
            !isScalarAfterVectorization(I, VF);
   }
 
   /// Decision that was taken during cost calculation for memory instruction.
   enum InstWidening {
     CM_Unknown,
     CM_Widen,         // For consecutive accesses with stride +1.
     CM_Widen_Reverse, // For consecutive accesses with stride -1.
     CM_Interleave,
     CM_GatherScatter,
     CM_Scalarize
   };
 
   /// Save vectorization decision \p W and \p Cost taken by the cost model for
   /// instruction \p I and vector width \p VF.
-  void setWideningDecision(Instruction *I, unsigned VF, InstWidening W,
+  void setWideningDecision(Instruction *I, ElementCount VF, InstWidening W,
                            unsigned Cost) {
-    assert(VF >= 2 && "Expected VF >=2");
+    assert(VF.isVector() && "Expected VF >=2");
     WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
   }
 
   /// Save vectorization decision \p W and \p Cost taken by the cost model for
   /// interleaving group \p Grp and vector width \p VF.
-  void setWideningDecision(const InterleaveGroup<Instruction> *Grp, unsigned VF,
-                           InstWidening W, unsigned Cost) {
-    assert(VF >= 2 && "Expected VF >=2");
+  void setWideningDecision(const InterleaveGroup<Instruction> *Grp,
+                           ElementCount VF, InstWidening W, unsigned Cost) {
+    assert(VF.isVector() && "Expected VF >=2");
     /// Broadcast this decicion to all instructions inside the group.
     /// But the cost will be assigned to one instruction only.
     for (unsigned i = 0; i < Grp->getFactor(); ++i) {
       if (auto *I = Grp->getMember(i)) {
         if (Grp->getInsertPos() == I)
           WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
         else
           WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0);
       }
     }
   }
 
   /// Return the cost model decision for the given instruction \p I and vector
   /// width \p VF. Return CM_Unknown if this instruction did not pass
   /// through the cost modeling.
-  InstWidening getWideningDecision(Instruction *I, unsigned VF) {
-    assert(VF >= 2 && "Expected VF >=2");
+  InstWidening getWideningDecision(Instruction *I, ElementCount VF) {
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
+    assert(VF.isVector() && "Expected VF >=2");
 
     // Cost model is not run in the VPlan-native path - return conservative
     // result until this changes.
     if (EnableVPlanNativePath)
       return CM_GatherScatter;
 
-    std::pair<Instruction *, unsigned> InstOnVF = std::make_pair(I, VF);
+    std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF);
     auto Itr = WideningDecisions.find(InstOnVF);
     if (Itr == WideningDecisions.end())
       return CM_Unknown;
     return Itr->second.first;
   }
 
   /// Return the vectorization cost for the given instruction \p I and vector
   /// width \p VF.
-  unsigned getWideningCost(Instruction *I, unsigned VF) {
-    assert(VF >= 2 && "Expected VF >=2");
-    std::pair<Instruction *, unsigned> InstOnVF = std::make_pair(I, VF);
+  unsigned getWideningCost(Instruction *I, ElementCount VF) {
+    assert(VF.isVector() && "Expected VF >=2");
+    std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF);
     assert(WideningDecisions.find(InstOnVF) != WideningDecisions.end() &&
            "The cost is not calculated");
     return WideningDecisions[InstOnVF].second;
   }
 
   /// Return True if instruction \p I is an optimizable truncate whose operand
   /// is an induction variable. Such a truncate will be removed by adding a new
   /// induction variable with the destination type.
-  bool isOptimizableIVTruncate(Instruction *I, unsigned VF) {
+  bool isOptimizableIVTruncate(Instruction *I, ElementCount VF) {
     // If the instruction is not a truncate, return false.
     auto *Trunc = dyn_cast<TruncInst>(I);
     if (!Trunc)
       return false;
 
     // Get the source and destination types of the truncate.
     Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF);
     Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF);
 
     // If the truncate is free for the given types, return false. Replacing a
     // free truncate with an induction variable would add an induction variable
     // update instruction to each iteration of the loop. We exclude from this
     // check the primary induction variable since it will need an update
     // instruction regardless.
     Value *Op = Trunc->getOperand(0);
     if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy))
       return false;
 
     // If the truncated value is not an induction variable, return false.
     return Legal->isInductionPhi(Op);
   }
 
   /// Collects the instructions to scalarize for each predicated instruction in
   /// the loop.
-  void collectInstsToScalarize(unsigned VF);
+  void collectInstsToScalarize(ElementCount VF);
 
   /// Collect Uniform and Scalar values for the given \p VF.
   /// The sets depend on CM decision for Load/Store instructions
   /// that may be vectorized as interleave, gather-scatter or scalarized.
-  void collectUniformsAndScalars(unsigned VF) {
+  void collectUniformsAndScalars(ElementCount VF) {
     // Do the analysis once.
-    if (VF == 1 || Uniforms.find(VF) != Uniforms.end())
+    if (VF.isScalar() || Uniforms.find(VF) != Uniforms.end())
       return;
     setCostBasedWideningDecision(VF);
     collectLoopUniforms(VF);
     collectLoopScalars(VF);
   }
 
   /// Returns true if the target machine supports masked store operation
   /// for the given \p DataType and kind of access to \p Ptr.
return (LI && isLegalMaskedGather(Ty, Align)) ||
            (SI && isLegalMaskedScatter(Ty, Align));
   }
 
   /// Returns true if \p I is an instruction that will be scalarized with
   /// predication. Such instructions include conditional stores and
   /// instructions that may divide by zero.
   /// If a non-zero VF has been calculated, we check if I will be scalarized
   /// predication for that VF.
-  bool isScalarWithPredication(Instruction *I, unsigned VF = 1);
+  bool isScalarWithPredication(Instruction *I,
+                               ElementCount VF = ElementCount::getFixed(1));
 
   // Returns true if \p I is an instruction that will be predicated either
   // through scalar predication or masked load/store or masked gather/scatter.
   // Superset of instructions that return true for isScalarWithPredication.
   bool isPredicatedInst(Instruction *I) {
     if (!blockNeedsPredication(I->getParent()))
       return false;
     // Loads and stores that need some form of masked operation are predicated
     // instructions.
     if (isa<LoadInst>(I) || isa<StoreInst>(I))
       return Legal->isMaskRequired(I);
     return isScalarWithPredication(I);
   }
 
   /// Returns true if \p I is a memory instruction with consecutive memory
   /// access that can be widened.
-  bool memoryInstructionCanBeWidened(Instruction *I, unsigned VF = 1);
+  bool
+  memoryInstructionCanBeWidened(Instruction *I,
+                                ElementCount VF = ElementCount::getFixed(1));
 
   /// Returns true if \p I is a memory instruction in an interleaved-group
   /// of memory accesses that can be vectorized with wide vector loads/stores
   /// and shuffles.
-  bool interleavedAccessCanBeWidened(Instruction *I, unsigned VF = 1);
+  bool
+  interleavedAccessCanBeWidened(Instruction *I,
+                                ElementCount VF = ElementCount::getFixed(1));
 
   /// Check if \p Instr belongs to any interleaved access group.
   bool isAccessInterleaved(Instruction *Instr) {
     return InterleaveInfo.isInterleaved(Instr);
   }
 
   /// Get the interleaved access group that \p Instr belongs to.
   const InterleaveGroup<Instruction> *
public:
   /// Returns true if the Phi is part of an inloop reduction.
   bool isInLoopReduction(PHINode *Phi) const {
     return InLoopReductionChains.count(Phi);
   }
 
   /// Estimate cost of an intrinsic call instruction CI if it were vectorized
   /// with factor VF.  Return the cost of the instruction, including
   /// scalarization overhead if it's needed.
-  unsigned getVectorIntrinsicCost(CallInst *CI, unsigned VF);
+  unsigned getVectorIntrinsicCost(CallInst *CI, ElementCount VF);
 
   /// Estimate cost of a call instruction CI if it were vectorized with factor
   /// VF. Return the cost of the instruction, including scalarization overhead
   /// if it's needed. The flag NeedToScalarize shows if the call needs to be
   /// scalarized -
   /// i.e. either vector version isn't available, or is too expensive.
-  unsigned getVectorCallCost(CallInst *CI, unsigned VF, bool &NeedToScalarize);
+  unsigned getVectorCallCost(CallInst *CI, ElementCount VF,
+                             bool &NeedToScalarize);
 
   /// Invalidates decisions already taken by the cost model.
   void invalidateCostModelingDecisions() {
     WideningDecisions.clear();
     Uniforms.clear();
     Scalars.clear();
   }
 
private:
   /// false, then all operations will be scalarized (i.e. no vectorization has
   /// actually taken place).
   using VectorizationCostTy = std::pair<unsigned, bool>;
 
   /// Returns the expected execution cost. The unit of the cost does
   /// not matter because we use the 'cost' units to compare different
   /// vector widths. The cost that is returned is *not* normalized by
   /// the factor width.
-  VectorizationCostTy expectedCost(unsigned VF);
+  VectorizationCostTy expectedCost(ElementCount VF);
 
   /// Returns the execution time cost of an instruction for a given vector
   /// width. Vector width of one means scalar.
-  VectorizationCostTy getInstructionCost(Instruction *I, unsigned VF);
+  VectorizationCostTy getInstructionCost(Instruction *I, ElementCount VF);
 
   /// The cost-computation logic from getInstructionCost which provides
   /// the vector type as an output parameter.
-  unsigned getInstructionCost(Instruction *I, unsigned VF, Type *&VectorTy);
+  unsigned getInstructionCost(Instruction *I, ElementCount VF, Type *&VectorTy);
 
   /// Calculate vectorization cost of memory instruction \p I.
-  unsigned getMemoryInstructionCost(Instruction *I, unsigned VF);
+  unsigned getMemoryInstructionCost(Instruction *I, ElementCount VF);
 
   /// The cost computation for scalarized memory instruction.
-  unsigned getMemInstScalarizationCost(Instruction *I, unsigned VF);
+  unsigned getMemInstScalarizationCost(Instruction *I, ElementCount VF);
 
   /// The cost computation for interleaving group of memory instructions.
-  unsigned getInterleaveGroupCost(Instruction *I, unsigned VF);
+  unsigned getInterleaveGroupCost(Instruction *I, ElementCount VF);
 
   /// The cost computation for Gather/Scatter instruction.
-  unsigned getGatherScatterCost(Instruction *I, unsigned VF);
+  unsigned getGatherScatterCost(Instruction *I, ElementCount VF);
 
   /// The cost computation for widening instruction \p I with consecutive
   /// memory access.
-  unsigned getConsecutiveMemOpCost(Instruction *I, unsigned VF);
+  unsigned getConsecutiveMemOpCost(Instruction *I, ElementCount VF);
 
   /// The cost calculation for Load/Store instruction \p I with uniform pointer -
   /// Load: scalar load + broadcast.
   /// Store: scalar store + (loop invariant value stored? 0 : extract of last
   /// element)
-  unsigned getUniformMemOpCost(Instruction *I, unsigned VF);
+  unsigned getUniformMemOpCost(Instruction *I, ElementCount VF);
 
   /// Estimate the overhead of scalarizing an instruction. This is a
   /// convenience wrapper for the type-based getScalarizationOverhead API.
-  unsigned getScalarizationOverhead(Instruction *I, unsigned VF);
+  unsigned getScalarizationOverhead(Instruction *I, ElementCount VF);
 
   /// Returns whether the instruction is a load or store and will be a emitted
   /// as a vector operation.
   bool isConsecutiveLoadOrStore(Instruction *I);
 
   /// Returns true if an artificially high cost for emulated masked memrefs
   /// should be used.
   bool useEmulatedMaskMemRefHack(Instruction *I);
private:
 
   /// All blocks of loop are to be masked to fold tail of scalar iterations.
   bool FoldTailByMasking = false;
 
   /// A map holding scalar costs for different vectorization factors. The
   /// presence of a cost for an instruction in the mapping indicates that the
   /// instruction will be scalarized when vectorizing with the associated
   /// vectorization factor. The entries are VF-ScalarCostTy pairs.
-  DenseMap<unsigned, ScalarCostsTy> InstsToScalarize;
+  DenseMap<ElementCount, ScalarCostsTy> InstsToScalarize;
 
   /// Holds the instructions known to be uniform after vectorization.
   /// The data is collected per VF.
-  DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> Uniforms;
+  DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Uniforms;
 
   /// Holds the instructions known to be scalar after vectorization.
   /// The data is collected per VF.
-  DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> Scalars;
+  DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Scalars;
 
   /// Holds the instructions (address computations) that are forced to be
   /// scalarized.
-  DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> ForcedScalars;
+  DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> ForcedScalars;
 
   /// PHINodes of the reductions that should be expanded in-loop along with
   /// their associated chains of reduction operations, in program order from top
   /// (PHI) to bottom
   ReductionChainMap InLoopReductionChains;
 
   /// Returns the expected difference in cost from scalarizing the expression
   /// feeding a predicated instruction \p PredInst. The instructions to
   /// scalarize and their scalar costs are collected in \p ScalarCosts. A
   /// non-negative return value implies the expression will be scalarized.
   /// Currently, only single-use chains are considered for scalarization.
   int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts,
-                              unsigned VF);
+                              ElementCount VF);
 
   /// Collect the instructions that are uniform after vectorization. An
   /// instruction is uniform if we represent it with a single scalar value in
   /// the vectorized loop corresponding to each vector iteration. Examples of
   /// uniform instructions include pointer operands of consecutive or
   /// interleaved memory accesses. Note that although uniformity implies an
   /// instruction will be scalar, the reverse is not true. In general, a
   /// scalarized instruction will be represented by VF scalar values in the
   /// vectorized loop, each corresponding to an iteration of the original
   /// scalar loop.
-  void collectLoopUniforms(unsigned VF);
+  void collectLoopUniforms(ElementCount VF);
 
   /// Collect the instructions that are scalar after vectorization. An
   /// instruction is scalar if it is known to be uniform or will be scalarized
   /// during vectorization. Non-uniform scalarized instructions will be
   /// represented by VF values in the vectorized loop, each corresponding to an
   /// iteration of the original scalar loop.
-  void collectLoopScalars(unsigned VF);
+  void collectLoopScalars(ElementCount VF);
 
   /// Keeps cost model vectorization decision and cost for instructions.
   /// Right now it is used for memory instructions only.
-  using DecisionList = DenseMap<std::pair<Instruction *, unsigned>,
+  using DecisionList = DenseMap<std::pair<Instruction *, ElementCount>,
                                 std::pair<InstWidening, unsigned>>;
 
   DecisionList WideningDecisions;
 
   /// Returns true if \p V is expected to be vectorized and it needs to be
   /// extracted.
-  bool needsExtract(Value *V, unsigned VF) const {
+  bool needsExtract(Value *V, ElementCount VF) const {
     Instruction *I = dyn_cast<Instruction>(V);
-    if (VF == 1 || !I || !TheLoop->contains(I) || TheLoop->isLoopInvariant(I))
+    if (VF.isScalar() || !I || !TheLoop->contains(I) ||
+        TheLoop->isLoopInvariant(I))
       return false;
 
     // Assume we can vectorize V (and hence we need extraction) if the
     // scalars are not computed yet. This can happen, because it is called
     // via getScalarizationOverhead from setCostBasedWideningDecision, before
     // the scalars are collected. That should be a safe assumption in most
     // cases, because we check if the operands have vectorizable types
     // beforehand in LoopVectorizationLegality.
     return Scalars.find(VF) == Scalars.end() ||
            !isScalarAfterVectorization(I, VF);
   };
 
   /// Returns a range containing only operands needing to be extracted.
   SmallVector<Value *, 4> filterExtractingOperands(Instruction::op_range Ops,
-                                                   unsigned VF) {
+                                                   ElementCount VF) {
     return SmallVector<Value *, 4>(make_filter_range(
         Ops, [this, VF](Value *V) { return this->needsExtract(V, VF); }));
   }
 
 public:
   /// The loop that we evaluate.
   Loop *TheLoop;
 
if (Step->getType()->isIntegerTy()) {
     MulOp = Instruction::Mul;
   } else {
     AddOp = II.getInductionOpcode();
     MulOp = Instruction::FMul;
   }
 
   // Multiply the vectorization factor by the step using integer or
   // floating-point arithmetic as appropriate.
-  Value *ConstVF = getSignedIntOrFpConstant(Step->getType(), VF);
+  Value *ConstVF = getSignedIntOrFpConstant(Step->getType(), VF.Min);
   Value *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, Step, ConstVF));
 
   // Create a vector splat to use in the induction update.
   //
   // FIXME: If the step is non-constant, we create the vector splat with
   //        IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't
   //        handle a constant vector splat.
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   Value *SplatVF = isa<Constant>(Mul)
-                       ? ConstantVector::getSplat(ElementCount::getFixed(VF),
-                                                  cast<Constant>(Mul))
+                       ? ConstantVector::getSplat(VF, cast<Constant>(Mul))
                        : Builder.CreateVectorSplat(VF, Mul);
   Builder.restoreIP(CurrIP);
 
   // We may need to add the step a number of times, depending on the unroll
   // factor. The last of those goes into the PHI.
   PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind",
                                     &*LoopVectorBody->getFirstInsertionPt());
   VecInd->setDebugLoc(EntryVal->getDebugLoc());
   Instruction *LastInduction = VecInd;
   for (unsigned Part = 0; Part < UF; ++Part) {
auto CreateScalarIV = [&](Value *&Step) -> Value * {
     return ScalarIV;
   };
 
   // Create the vector values from the scalar IV, in the absence of creating a
   // vector IV.
   auto CreateSplatIV = [&](Value *ScalarIV, Value *Step) {
     Value *Broadcasted = getBroadcastInstrs(ScalarIV);
     for (unsigned Part = 0; Part < UF; ++Part) {
-      Value *EntryPart =
-          getStepVector(Broadcasted, VF * Part, Step, ID.getInductionOpcode());
+      assert(!VF.Scalable && "scalable vectors not yet supported.");
+      Value *EntryPart = getStepVector(Broadcasted, VF.Min * Part, Step,
+                                       ID.getInductionOpcode());
       VectorLoopValueMap.setVectorValue(EntryVal, Part, EntryPart);
       if (Trunc)
         addMetadata(EntryPart, Trunc);
       recordVectorLoopValueForInductionCast(ID, EntryVal, EntryPart, Part);
     }
   };
 
   // Now do the actual transformations, and start with creating the step value.
   Value *Step = CreateStepValue(ID.getStep());
-  if (VF <= 1) {
+  if (VF.isZero() || VF.isScalar()) {
     Value *ScalarIV = CreateScalarIV(Step);
     CreateSplatIV(ScalarIV, Step);
     return;
   }
 
   // Determine if we want a scalar version of the induction variable. This is
   // true if the induction variable itself is not widened, or if it has at
   // least one user in the loop that is not widened.
if (isa<Instruction>(BOp))
     cast<Instruction>(BOp)->setFastMathFlags(Flags);
   return BOp;
 }
 
 void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step,
                                            Instruction *EntryVal,
                                            const InductionDescriptor &ID) {
   // We shouldn't have to build scalar steps if we aren't vectorizing.
-  assert(VF > 1 && "VF should be greater than one");
-
+  assert(VF.isVector() && "VF should be greater than one");
+  assert(!VF.Scalable &&
+         "the code below assumes a fixed number of elements at compile time");
   // Get the value type and ensure it and the step have the same integer type.
   Type *ScalarIVTy = ScalarIV->getType()->getScalarType();
   assert(ScalarIVTy == Step->getType() &&
          "Val and Step should have the same type");
 
   // We build scalar steps for both integer and floating-point induction
   // variables. Here, we determine the kind of arithmetic we will perform.
   Instruction::BinaryOps AddOp;
   Instruction::BinaryOps MulOp;
   if (ScalarIVTy->isIntegerTy()) {
     AddOp = Instruction::Add;
     MulOp = Instruction::Mul;
   } else {
     AddOp = ID.getInductionOpcode();
     MulOp = Instruction::FMul;
   }
 
   // Determine the number of scalars we need to generate for each unroll
   // iteration. If EntryVal is uniform, we only need to generate the first
   // lane. Otherwise, we generate all VF values.
   unsigned Lanes =
-      Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF) ? 1
-                                                                         : VF;
+      Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF)
+          ? 1
+          : VF.Min;
   // Compute the scalar steps and save the results in VectorLoopValueMap.
   for (unsigned Part = 0; Part < UF; ++Part) {
     for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
-      auto *StartIdx = getSignedIntOrFpConstant(ScalarIVTy, VF * Part + Lane);
+      auto *StartIdx =
+          getSignedIntOrFpConstant(ScalarIVTy, VF.Min * Part + Lane);
       auto *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, StartIdx, Step));
       auto *Add = addFastMathFlag(Builder.CreateBinOp(AddOp, ScalarIV, Mul));
       VectorLoopValueMap.setScalarValue(EntryVal, {Part, Lane}, Add);
       recordVectorLoopValueForInductionCast(ID, EntryVal, Add, Part, Lane);
     }
   }
 }
 
if (VF == 1) {
       VectorLoopValueMap.setVectorValue(V, Part, ScalarValue);
       return ScalarValue;
     }
 
     // Get the last scalar instruction we generated for V and Part. If the value
     // is known to be uniform after vectorization, this corresponds to lane zero
     // of the Part unroll iteration. Otherwise, the last instruction is the one
     // we created for the last vector lane of the Part unroll iteration.
-    unsigned LastLane = Cost->isUniformAfterVectorization(I, VF) ? 0 : VF - 1;
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
+    unsigned LastLane =
+        Cost->isUniformAfterVectorization(I, VF) ? 0 : VF.Min - 1;
     auto *LastInst = cast<Instruction>(
         VectorLoopValueMap.getScalarValue(V, {Part, LastLane}));
 
     // Set the insert point after the last scalarized instruction. This ensures
     // the insertelement sequence will directly follow the scalar definitions.
     auto OldIP = Builder.saveIP();
     auto NewIP = std::next(BasicBlock::iterator(LastInst));
     Builder.SetInsertPoint(&*NewIP);
 
     // However, if we are vectorizing, we need to construct the vector values.
     // If the value is known to be uniform after vectorization, we can just
     // broadcast the scalar value corresponding to lane zero for each unroll
     // iteration. Otherwise, we construct the vector values using insertelement
     // instructions. Since the resulting vectors are stored in
     // VectorLoopValueMap, we will only generate the insertelements once.
     Value *VectorValue = nullptr;
     if (Cost->isUniformAfterVectorization(I, VF)) {
       VectorValue = getBroadcastInstrs(ScalarValue);
       VectorLoopValueMap.setVectorValue(V, Part, VectorValue);
     } else {
       // Initialize packing with insertelements to start from undef.
-      Value *Undef = UndefValue::get(FixedVectorType::get(V->getType(), VF));
+      assert(!VF.Scalable && "VF is assumed to be non scalable.");
+      Value *Undef = UndefValue::get(VectorType::get(V->getType(), VF));
       VectorLoopValueMap.setVectorValue(V, Part, Undef);
-      for (unsigned Lane = 0; Lane < VF; ++Lane)
+      for (unsigned Lane = 0; Lane < VF.Min; ++Lane)
         packScalarIntoVectorValue(V, {Part, Lane});
       VectorValue = VectorLoopValueMap.getVectorValue(V, Part);
     }
     Builder.restoreIP(OldIP);
     return VectorValue;
   }
 
   // If this scalar is unknown, assume that it is a constant or that it is
void InnerLoopVectorizer::packScalarIntoVectorValue(
   Value *VectorValue = VectorLoopValueMap.getVectorValue(V, Instance.Part);
   VectorValue = Builder.CreateInsertElement(VectorValue, ScalarInst,
                                             Builder.getInt32(Instance.Lane));
   VectorLoopValueMap.resetVectorValue(V, Instance.Part, VectorValue);
 }
 
 Value *InnerLoopVectorizer::reverseVector(Value *Vec) {
   assert(Vec->getType()->isVectorTy() && "Invalid type");
+  assert(!VF.Scalable && "Cannot reverse scalable vectors");
   SmallVector<int, 8> ShuffleMask;
-  for (unsigned i = 0; i < VF; ++i)
-    ShuffleMask.push_back(VF - i - 1);
+  for (unsigned i = 0; i < VF.Min; ++i)
+    ShuffleMask.push_back(VF.Min - i - 1);
 
   return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()),
                                      ShuffleMask, "reverse");
 }
 
 // Return whether we allow using masked interleave-groups (for dealing with
 // strided loads/stores that reside in predicated blocks, or for dealing
 // with gaps).
void InnerLoopVectorizer::vectorizeInterleaveGroup(
     const InterleaveGroup<Instruction> *Group, VPTransformState &State,
     VPValue *Addr, VPValue *BlockInMask) {
   Instruction *Instr = Group->getInsertPos();
   const DataLayout &DL = Instr->getModule()->getDataLayout();
 
   // Prepare for the vector type of the interleaved load/store.
   Type *ScalarTy = getMemInstValueType(Instr);
   unsigned InterleaveFactor = Group->getFactor();
-  auto *VecTy = FixedVectorType::get(ScalarTy, InterleaveFactor * VF);
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
+  auto *VecTy = VectorType::get(ScalarTy, VF * InterleaveFactor);
 
   // Prepare for the new pointers.
   SmallVector<Value *, 2> AddrParts;
   unsigned Index = Group->getIndex(Instr);
 
   // TODO: extend the masked interleaved-group support to reversed access.
   assert((!BlockInMask || !Group->isReverse()) &&
          "Reversed masked interleave-group not supported.");
 
   // If the group is reverse, adjust the index to refer to the last vector lane
   // instead of the first. We adjust the index from the first vector lane,
   // rather than directly getting the pointer for lane VF - 1, because the
   // pointer operand of the interleaved access is supposed to be uniform. For
   // uniform instructions, we're only required to generate a value for the
   // first vector lane in each unroll iteration.
+  assert(!VF.Scalable &&
+         "scalable vector reverse operation is not implemented");
   if (Group->isReverse())
-    Index += (VF - 1) * Group->getFactor();
+    Index += (VF.Min - 1) * Group->getFactor();
 
   for (unsigned Part = 0; Part < UF; Part++) {
     Value *AddrPart = State.get(Addr, {Part, 0});
     setDebugLocFromInst(Builder, AddrPart);
 
     // Notice current instruction could be any index. Need to adjust the address
     // to the member of index 0.
     //
for (unsigned Part = 0; Part < UF; Part++) {
     AddrParts.push_back(Builder.CreateBitCast(AddrPart, PtrTy));
   }
 
   setDebugLocFromInst(Builder, Instr);
   Value *UndefVec = UndefValue::get(VecTy);
 
   Value *MaskForGaps = nullptr;
   if (Group->requiresScalarEpilogue() && !Cost->isScalarEpilogueAllowed()) {
-    MaskForGaps = createBitMaskForGaps(Builder, VF, *Group);
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
+    MaskForGaps = createBitMaskForGaps(Builder, VF.Min, *Group);
     assert(MaskForGaps && "Mask for Gaps is required but it is null");
   }
 
   // Vectorize the interleaved load group.
   if (isa<LoadInst>(Instr)) {
     // For each unroll part, create a wide load for the group.
     SmallVector<Value *, 2> NewLoads;
     for (unsigned Part = 0; Part < UF; Part++) {
       Instruction *NewLoad;
       if (BlockInMask || MaskForGaps) {
         assert(useMaskedInterleavedAccesses(*TTI) &&
                "masked interleaved groups are not allowed.");
         Value *GroupMask = MaskForGaps;
         if (BlockInMask) {
           Value *BlockInMaskPart = State.get(BlockInMask, Part);
           auto *Undefs = UndefValue::get(BlockInMaskPart->getType());
+          assert(!VF.Scalable && "scalable vectors not yet supported.");
           Value *ShuffledMask = Builder.CreateShuffleVector(
               BlockInMaskPart, Undefs,
-              createReplicatedMask(InterleaveFactor, VF), "interleaved.mask");
+              createReplicatedMask(InterleaveFactor, VF.Min),
+              "interleaved.mask");
           GroupMask = MaskForGaps
                           ? Builder.CreateBinOp(Instruction::And, ShuffledMask,
                                                 MaskForGaps)
                           : ShuffledMask;
         }
         NewLoad =
             Builder.CreateMaskedLoad(AddrParts[Part], Group->getAlign(),
                                      GroupMask, UndefVec, "wide.masked.vec");
if (isa<LoadInst>(Instr)) {
     // wide loads.
     for (unsigned I = 0; I < InterleaveFactor; ++I) {
       Instruction *Member = Group->getMember(I);
 
       // Skip the gaps in the group.
       if (!Member)
         continue;
 
-      auto StrideMask = createStrideMask(I, InterleaveFactor, VF);
+      assert(!VF.Scalable && "scalable vectors not yet supported.");
+      auto StrideMask = createStrideMask(I, InterleaveFactor, VF.Min);
       for (unsigned Part = 0; Part < UF; Part++) {
         Value *StridedVec = Builder.CreateShuffleVector(
             NewLoads[Part], UndefVec, StrideMask, "strided.vec");
 
         // If this member has different type, cast the result type.
         if (Member->getType() != ScalarTy) {
-          VectorType *OtherVTy = FixedVectorType::get(Member->getType(), VF);
+          assert(!VF.Scalable && "VF is assumed to be non scalable.");
+          VectorType *OtherVTy = VectorType::get(Member->getType(), VF);
           StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL);
         }
 
         if (Group->isReverse())
           StridedVec = reverseVector(StridedVec);
 
         VectorLoopValueMap.setVectorValue(Member, Part, StridedVec);
       }
     }
     return;
   }
 
   // The sub vector type for current instruction.
-  auto *SubVT = FixedVectorType::get(ScalarTy, VF);
+  assert(!VF.Scalable && "VF is assumed to be non scalable.");
+  auto *SubVT = VectorType::get(ScalarTy, VF);
 
   // Vectorize the interleaved store group.
   for (unsigned Part = 0; Part < UF; Part++) {
     // Collect the stored vector from each member.
     SmallVector<Value *, 4> StoredVecs;
     for (unsigned i = 0; i < InterleaveFactor; i++) {
       // Interleaved store group doesn't allow a gap, so each index has a member
       Instruction *Member = Group->getMember(i);
for (unsigned i = 0; i < InterleaveFactor; i++) {
 
       StoredVecs.push_back(StoredVec);
     }
 
     // Concatenate all vectors into a wide vector.
     Value *WideVec = concatenateVectors(Builder, StoredVecs);
 
     // Interleave the elements in the wide vector.
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
     Value *IVec = Builder.CreateShuffleVector(
-        WideVec, UndefVec, createInterleaveMask(VF, InterleaveFactor),
+        WideVec, UndefVec, createInterleaveMask(VF.Min, InterleaveFactor),
         "interleaved.vec");
 
     Instruction *NewStoreInstr;
     if (BlockInMask) {
       Value *BlockInMaskPart = State.get(BlockInMask, Part);
       auto *Undefs = UndefValue::get(BlockInMaskPart->getType());
       Value *ShuffledMask = Builder.CreateShuffleVector(
-          BlockInMaskPart, Undefs, createReplicatedMask(InterleaveFactor, VF),
-          "interleaved.mask");
+          BlockInMaskPart, Undefs,
+          createReplicatedMask(InterleaveFactor, VF.Min), "interleaved.mask");
       NewStoreInstr = Builder.CreateMaskedStore(
           IVec, AddrParts[Part], Group->getAlign(), ShuffledMask);
     }
     else
       NewStoreInstr =
           Builder.CreateAlignedStore(IVec, AddrParts[Part], Group->getAlign());
 
     Group->addMetadata(NewStoreInstr);
void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr,
   LoopVectorizationCostModel::InstWidening Decision =
       Cost->getWideningDecision(Instr, VF);
   assert((Decision == LoopVectorizationCostModel::CM_Widen ||
           Decision == LoopVectorizationCostModel::CM_Widen_Reverse ||
           Decision == LoopVectorizationCostModel::CM_GatherScatter) &&
          "CM decision is not to widen the memory instruction");
 
   Type *ScalarDataTy = getMemInstValueType(Instr);
-  auto *DataTy = FixedVectorType::get(ScalarDataTy, VF);
+
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
+  auto *DataTy = VectorType::get(ScalarDataTy, VF);
   const Align Alignment = getLoadStoreAlignment(Instr);
 
   // Determine if the pointer operand of the access is either consecutive or
   // reverse consecutive.
   bool Reverse = (Decision == LoopVectorizationCostModel::CM_Widen_Reverse);
   bool ConsecutiveStride =
       Reverse || (Decision == LoopVectorizationCostModel::CM_Widen);
   bool CreateGatherScatter =
const auto CreateVecPtr = [&](unsigned Part, Value *Ptr) -> Value * {
 
     bool InBounds = false;
     if (auto *gep = dyn_cast<GetElementPtrInst>(Ptr->stripPointerCasts()))
       InBounds = gep->isInBounds();
 
     if (Reverse) {
       // If the address is consecutive but reversed, then the
       // wide store needs to start at the last vector element.
-      PartPtr = cast<GetElementPtrInst>(
-          Builder.CreateGEP(ScalarDataTy, Ptr, Builder.getInt32(-Part * VF)));
+      PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP(
+          ScalarDataTy, Ptr, Builder.getInt32(-Part * VF.Min)));
       PartPtr->setIsInBounds(InBounds);
-      PartPtr = cast<GetElementPtrInst>(
-          Builder.CreateGEP(ScalarDataTy, PartPtr, Builder.getInt32(1 - VF)));
+      PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP(
+          ScalarDataTy, PartPtr, Builder.getInt32(1 - VF.Min)));
       PartPtr->setIsInBounds(InBounds);
       if (isMaskRequired) // Reverse of a null all-one mask is a null mask.
         BlockInMaskParts[Part] = reverseVector(BlockInMaskParts[Part]);
     } else {
-      PartPtr = cast<GetElementPtrInst>(
-          Builder.CreateGEP(ScalarDataTy, Ptr, Builder.getInt32(Part * VF)));
+      PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP(
+          ScalarDataTy, Ptr, Builder.getInt32(Part * VF.Min)));
       PartPtr->setIsInBounds(InBounds);
     }
 
     unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace();
     return Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
   };
 
   // Handle Stores:

 Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) {
   if (VectorTripCount)
     return VectorTripCount;
 
   Value *TC = getOrCreateTripCount(L);
   IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
 
   Type *Ty = TC->getType();
-  Constant *Step = ConstantInt::get(Ty, VF * UF);
+  // This is where we can make the step a runtime constant.
+  assert(!VF.Scalable && "scalable vectorization is not supported yet");
+  Constant *Step = ConstantInt::get(Ty, VF.Min * UF);
 
   // If the tail is to be folded by masking, round the number of iterations N
   // up to a multiple of Step instead of rounding down. This is done by first
   // adding Step-1 and then rounding down. Note that it's ok if this addition
   // overflows: the vector induction variable will eventually wrap to zero given
   // that it starts at zero and its Step is a power of two; the loop will then
   // exit, with the last early-exit vector comparison also producing all-true.
   if (Cost->foldTailByMasking()) {
-    assert(isPowerOf2_32(VF * UF) &&
+    assert(isPowerOf2_32(VF.Min * UF) &&
            "VF*UF must be a power of 2 when folding tail by masking");
-    TC = Builder.CreateAdd(TC, ConstantInt::get(Ty, VF * UF - 1), "n.rnd.up");
+    TC = Builder.CreateAdd(TC, ConstantInt::get(Ty, VF.Min * UF - 1),
+                           "n.rnd.up");
   }
 
   // Now we need to generate the expression for the part of the loop that the
   // vectorized body will execute. This is equal to N - (N % Step) if scalar
   // iterations are not required for correctness, or N - Step, otherwise. Step
   // is equal to the vectorization factor (number of SIMD elements) times the
   // unroll factor (number of SIMD instructions).
   Value *R = Builder.CreateURem(TC, Step, "n.mod.vf");
 
   // If there is a non-reversed interleaved group that may speculatively access
   // memory out-of-bounds, we need to ensure that there will be at least one
   // iteration of the scalar epilogue loop. Thus, if the step evenly divides
   // the trip count, we set the remainder to be equal to the step. If the step
   // does not evenly divide the trip count, no adjustment is necessary since
   // there will already be scalar iterations. Note that the minimum iterations
   // check ensures that N >= Step.
-  if (VF > 1 && Cost->requiresScalarEpilogue()) {
+  if (VF.isVector() && Cost->requiresScalarEpilogue()) {
     auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0));
     R = Builder.CreateSelect(IsZero, Step, R);
   }
 
   VectorTripCount = Builder.CreateSub(TC, R, "n.vec");
 
   return VectorTripCount;
 }
 
 Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy,
                                                    const DataLayout &DL) {
   // Verify that V is a vector type with same number of elements as DstVTy.
+  assert(isa<FixedVectorType>(DstVTy) &&
+         "Vector type is assumed to be fixed width.");
   unsigned VF = DstVTy->getNumElements();
   VectorType *SrcVecTy = cast<VectorType>(V->getType());
   assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match");
   Type *SrcElemTy = SrcVecTy->getElementType();
   Type *DstElemTy = DstVTy->getElementType();
   assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) &&
          "Vector elements must have same size");
 
void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L,
   // vector trip count is zero. This check also covers the case where adding one
   // to the backedge-taken count overflowed leading to an incorrect trip count
   // of zero. In this case we will also jump to the scalar loop.
   auto P = Cost->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE
                                           : ICmpInst::ICMP_ULT;
 
   // If tail is to be folded, vector loop takes care of all iterations.
   Value *CheckMinIters = Builder.getFalse();
-  if (!Cost->foldTailByMasking())
+  if (!Cost->foldTailByMasking()) {
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
     CheckMinIters = Builder.CreateICmp(
-        P, Count, ConstantInt::get(Count->getType(), VF * UF),
+        P, Count, ConstantInt::get(Count->getType(), VF.Min * UF),
         "min.iters.check");
-
+  }
   // Create new preheader for vector loop.
   LoopVectorPreHeader =
       SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), DT, LI, nullptr,
                  "vector.ph");
 
   assert(DT->properlyDominates(DT->getNode(TCCheckBlock),
                                DT->getNode(Bypass)->getIDom()) &&
          "TC check is expected to dominate Bypass");
BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() {
   //   - counts from zero, stepping by one
   //   - is the size of the widest induction variable type
   // then we create a new one.
   OldInduction = Legal->getPrimaryInduction();
   Type *IdxTy = Legal->getWidestInductionType();
   Value *StartIdx = ConstantInt::get(IdxTy, 0);
   // The loop step is equal to the vectorization factor (num of SIMD elements)
   // times the unroll factor (num of SIMD instructions).
-  Constant *Step = ConstantInt::get(IdxTy, VF * UF);
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
+  Constant *Step = ConstantInt::get(IdxTy, VF.Min * UF);
   Value *CountRoundDown = getOrCreateVectorTripCount(Lp);
   Induction =
       createInductionVariable(Lp, StartIdx, CountRoundDown, Step,
                               getDebugLocFromInstOrOperands(OldInduction));
 
   // Emit phis for the new starting index of the scalar loop.
   createInductionResumeValues(Lp, CountRoundDown);
 
if (Instruction *V = CSEMap.lookup(In)) {
       continue;
     }
 
     CSEMap[In] = In;
   }
 }
 
 unsigned LoopVectorizationCostModel::getVectorCallCost(CallInst *CI,
-                                                       unsigned VF,
+                                                       ElementCount VF,
                                                        bool &NeedToScalarize) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   Function *F = CI->getCalledFunction();
   Type *ScalarRetTy = CI->getType();
   SmallVector<Type *, 4> Tys, ScalarTys;
   for (auto &ArgOp : CI->arg_operands())
     ScalarTys.push_back(ArgOp->getType());
 
   // Estimate cost of scalarized vector call. The source operands are assumed
   // to be vectors, so we need to extract individual elements from there,
   // execute VF scalar calls, and then gather the result into the vector return
   // value.
   unsigned ScalarCallCost = TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys,
                                                  TTI::TCK_RecipThroughput);
-  if (VF == 1)
+  if (VF.isScalar())
     return ScalarCallCost;
 
   // Compute corresponding vector type for return value and arguments.
   Type *RetTy = ToVectorTy(ScalarRetTy, VF);
   for (Type *ScalarTy : ScalarTys)
     Tys.push_back(ToVectorTy(ScalarTy, VF));
 
   // Compute costs of unpacking argument values for the scalar calls and
   // packing the return values to a vector.
   unsigned ScalarizationCost = getScalarizationOverhead(CI, VF);
 
-  unsigned Cost = ScalarCallCost * VF + ScalarizationCost;
+  unsigned Cost = ScalarCallCost * VF.Min + ScalarizationCost;
 
   // If we can't emit a vector call for this function, then the currently found
   // cost is the cost we need to return.
   NeedToScalarize = true;
-  VFShape Shape =
-      VFShape::get(*CI, ElementCount::getFixed(VF), false /*HasGlobalPred*/);
+  VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/);
   Function *VecFunc = VFDatabase(*CI).getVectorizedFunction(Shape);
 
   if (!TLI || CI->isNoBuiltin() || !VecFunc)
     return Cost;
 
   // If the corresponding vector cost is cheaper, return its cost.
   unsigned VectorCallCost = TTI.getCallInstrCost(nullptr, RetTy, Tys,
                                                  TTI::TCK_RecipThroughput);
   if (VectorCallCost < Cost) {
     NeedToScalarize = false;
     return VectorCallCost;
   }
   return Cost;
 }
 
 unsigned LoopVectorizationCostModel::getVectorIntrinsicCost(CallInst *CI,
-                                                            unsigned VF) {
+                                                            ElementCount VF) {
   Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
   assert(ID && "Expected intrinsic call!");
 
   IntrinsicCostAttributes CostAttrs(ID, *CI, VF);
   return TTI.getIntrinsicInstrCost(CostAttrs,
                                    TargetTransformInfo::TCK_RecipThroughput);
 }
 
for (unsigned Part = 0; Part < UF; ++Part) {
       }
     }
   }
 }
 
 void InnerLoopVectorizer::fixVectorizedLoop() {
   // Insert truncates and extends for any truncated instructions as hints to
   // InstCombine.
-  if (VF > 1)
+  if (VF.isVector())
     truncateToMinimalBitwidths();
 
   // Fix widened non-induction PHIs by setting up the PHI operands.
   if (OrigPHIsToFix.size()) {
     assert(EnableVPlanNativePath &&
            "Unexpected non-induction PHIs for fixup in non VPlan-native path");
     fixNonInductionPHIs();
   }
void InnerLoopVectorizer::fixVectorizedLoop() {
   // loop iterations are now distributed among them. Note that original loop
   // represented by LoopScalarBody becomes remainder loop after vectorization.
   //
   // For cases like foldTailByMasking() and requiresScalarEpiloque() we may
   // end up getting slightly roughened result but that should be OK since
   // profile is not inherently precise anyway. Note also possible bypass of
   // vector code caused by legality checks is ignored, assigning all the weight
   // to the vector loop, optimistically.
+  assert(!VF.Scalable &&
+         "cannot use scalable ElementCount to determine unroll factor");
   setProfileInfoAfterUnrolling(LI->getLoopFor(LoopScalarBody),
                                LI->getLoopFor(LoopVectorBody),
-                               LI->getLoopFor(LoopScalarBody), VF * UF);
+                               LI->getLoopFor(LoopScalarBody), VF.Min * UF);
 }
 
 void InnerLoopVectorizer::fixCrossIterationPHIs() {
   // In order to support recurrences we need to be able to vectorize Phi nodes.
   // Phi nodes have cycles, so we need to vectorize them in two stages. This is
   // stage #2: We now need to fix the recurrences by adding incoming edges to
   // the currently empty PHI nodes. At this point every instruction in the
   // original loop is widened to a vector form so we can use them to construct
void InnerLoopVectorizer::fixFirstOrderRecurrence(PHINode *Phi) {
   auto *Latch = OrigLoop->getLoopLatch();
 
   // Get the initial and previous values of the scalar recurrence.
   auto *ScalarInit = Phi->getIncomingValueForBlock(Preheader);
   auto *Previous = Phi->getIncomingValueForBlock(Latch);
 
   // Create a vector from the initial value.
   auto *VectorInit = ScalarInit;
-  if (VF > 1) {
+  if (VF.isVector()) {
     Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
+    assert(!VF.Scalable && "VF is assumed to be non scalable.");
     VectorInit = Builder.CreateInsertElement(
-        UndefValue::get(FixedVectorType::get(VectorInit->getType(), VF)),
-        VectorInit, Builder.getInt32(VF - 1), "vector.recur.init");
+        UndefValue::get(VectorType::get(VectorInit->getType(), VF)), VectorInit,
+        Builder.getInt32(VF.Min - 1), "vector.recur.init");
   }
 
   // We constructed a temporary phi node in the first phase of vectorization.
   // This phi node will eventually be deleted.
   Builder.SetInsertPoint(
       cast<Instruction>(VectorLoopValueMap.getVectorValue(Phi, 0)));
 
   // Create a phi node for the new recurrence. The current value will either be
if (isa<PHINode>(PreviousLastPart))
       InsertPt = PreviousInst->getParent()->getFirstInsertionPt();
     else
       InsertPt = ++PreviousInst->getIterator();
   }
   Builder.SetInsertPoint(&*InsertPt);
 
   // We will construct a vector for the recurrence by combining the values for
   // the current and previous iterations. This is the required shuffle mask.
-  SmallVector<int, 8> ShuffleMask(VF);
-  ShuffleMask[0] = VF - 1;
-  for (unsigned I = 1; I < VF; ++I)
-    ShuffleMask[I] = I + VF - 1;
+  assert(!VF.Scalable);
+  SmallVector<int, 8> ShuffleMask(VF.Min);
+  ShuffleMask[0] = VF.Min - 1;
+  for (unsigned I = 1; I < VF.Min; ++I)
+    ShuffleMask[I] = I + VF.Min - 1;
 
   // The vector from which to take the initial value for the current iteration
   // (actual or unrolled). Initially, this is the vector phi node.
   Value *Incoming = VecPhi;
 
   // Shuffle the current and previous vector and update the vector parts.
   for (unsigned Part = 0; Part < UF; ++Part) {
     Value *PreviousPart = getOrCreateVectorValue(Previous, Part);
     Value *PhiPart = VectorLoopValueMap.getVectorValue(Phi, Part);
-    auto *Shuffle = VF > 1 ? Builder.CreateShuffleVector(Incoming, PreviousPart,
-                                                         ShuffleMask)
-                           : Incoming;
+    auto *Shuffle =
+        VF.isVector()
+            ? Builder.CreateShuffleVector(Incoming, PreviousPart, ShuffleMask)
     PhiPart->replaceAllUsesWith(Shuffle);
     cast<Instruction>(PhiPart)->eraseFromParent();
     VectorLoopValueMap.resetVectorValue(Phi, Part, Shuffle);
     Incoming = PreviousPart;
   }
 
   // Fix the latch value of the new recurrence in the vector loop.
   VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
 
   // Extract the last vector element in the middle block. This will be the
   // initial value for the recurrence when jumping to the scalar loop.
   auto *ExtractForScalar = Incoming;
-  if (VF > 1) {
+  if (VF.isVector()) {
     Builder.SetInsertPoint(LoopMiddleBlock->getTerminator());
     ExtractForScalar = Builder.CreateExtractElement(
-        ExtractForScalar, Builder.getInt32(VF - 1), "vector.recur.extract");
+        ExtractForScalar, Builder.getInt32(VF.Min - 1), "vector.recur.extract");
   }
   // Extract the second last element in the middle block if the
   // Phi is used outside the loop. We need to extract the phi itself
   // and not the last element (the phi update in the current iteration). This
   // will be the value when jumping to the exit block from the LoopMiddleBlock,
   // when the scalar loop is not run at all.
   Value *ExtractForPhiUsedOutsideLoop = nullptr;
-  if (VF > 1)
+  if (VF.isVector())
     ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement(
-        Incoming, Builder.getInt32(VF - 2), "vector.recur.extract.for.phi");
+        Incoming, Builder.getInt32(VF.Min - 2), "vector.recur.extract.for.phi");
   // When loop is unrolled without vectorizing, initialize
   // ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value of
   // `Incoming`. This is analogous to the vectorized case above: extracting the
   // second last element when VF > 1.
   else if (UF > 1)
     ExtractForPhiUsedOutsideLoop = getOrCreateVectorValue(Previous, UF - 2);
 
   // Fix the initial value of the original recurrence in the scalar loop.
if (RK == RecurrenceDescriptor::RK_IntegerMinMax ||
     Constant *Iden = RecurrenceDescriptor::getRecurrenceIdentity(
         RK, VecTy->getScalarType());
     if (VF == 1 || IsInLoopReductionPhi) {
       Identity = Iden;
       // This vector is the Identity vector where the first element is the
       // incoming scalar reduction.
       VectorStart = ReductionStartValue;
     } else {
-      Identity = ConstantVector::getSplat(ElementCount::getFixed(VF), Iden);
+      Identity = ConstantVector::getSplat(VF, Iden);
 
       // This vector is the Identity vector where the first element is the
       // incoming scalar reduction.
       VectorStart =
         Builder.CreateInsertElement(Identity, ReductionStartValue, Zero);
     }
   }
 
for (unsigned Part = 0; Part < UF; ++Part) {
             LI->getLoopFor(LoopVectorBody)->getLoopLatch(), Sel);
       }
     }
   }
 
   // If the vector reduction can be performed in a smaller type, we truncate
   // then extend the loop exit value to enable InstCombine to evaluate the
   // entire expression in the smaller type.
-  if (VF > 1 && Phi->getType() != RdxDesc.getRecurrenceType()) {
+  if (VF.isVector() && Phi->getType() != RdxDesc.getRecurrenceType()) {
     assert(!IsInLoopReductionPhi && "Unexpected truncated inloop reduction!");
-    Type *RdxVecTy = FixedVectorType::get(RdxDesc.getRecurrenceType(), VF);
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
+    Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF);
     Builder.SetInsertPoint(
         LI->getLoopFor(LoopVectorBody)->getLoopLatch()->getTerminator());
     VectorParts RdxParts(UF);
     for (unsigned Part = 0; Part < UF; ++Part) {
       RdxParts[Part] = VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
       Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
       Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy)
                                         : Builder.CreateZExt(Trunc, VecTy);
if (Op != Instruction::ICmp && Op != Instruction::FCmp)
           RdxDesc.getFastMathFlags());
     else
       ReducedPartRdx = createMinMaxOp(Builder, MinMaxKind, ReducedPartRdx,
                                       RdxPart);
   }
 
   // Create the reduction after the loop. Note that inloop reductions create the
   // target reduction in the loop using a Reduction recipe.
-  if (VF > 1 && !IsInLoopReductionPhi) {
+  if (VF.isVector() && !IsInLoopReductionPhi) {
     bool NoNaN = Legal->hasFunNoNaNAttr();
     ReducedPartRdx =
         createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx, NoNaN);
     // If the reduction can be performed in a smaller type, we need to extend
     // the reduction to the wider type before we branch to the original loop.
     if (Phi->getType() != RdxDesc.getRecurrenceType())
       ReducedPartRdx =
         RdxDesc.isSigned()
for (User *U : Cur->users()) {
       if ((Cur != LoopExitInstr || OrigLoop->contains(UI->getParent())) &&
           Visited.insert(UI).second)
         Worklist.push_back(UI);
     }
   }
 }
 
 void InnerLoopVectorizer::fixLCSSAPHIs() {
+  assert(!VF.Scalable && "the code below assumes fixed width vectors");
   for (PHINode &LCSSAPhi : LoopExitBlock->phis()) {
     if (LCSSAPhi.getNumIncomingValues() == 1) {
       auto *IncomingValue = LCSSAPhi.getIncomingValue(0);
       // Non-instruction incoming values will have only one value.
       unsigned LastLane = 0;
-      if (isa<Instruction>(IncomingValue)) 
-          LastLane = Cost->isUniformAfterVectorization(
-                         cast<Instruction>(IncomingValue), VF)
-                         ? 0
-                         : VF - 1;
+      if (isa<Instruction>(IncomingValue))
+                       : VF.Min - 1;
       // Can be a loop invariant incoming value or the last scalar value to be
       // extracted from the vectorized loop.
       Builder.SetInsertPoint(LoopMiddleBlock->getTerminator());
       Value *lastIncomingValue =
           getOrCreateScalarValue(IncomingValue, { UF - 1, LastLane });
       LCSSAPhi.addIncoming(lastIncomingValue, LoopMiddleBlock);
     }
   }
for (unsigned i = 0; i < NumIncomingValues; ++i) {
       // Scalar incoming value may need a broadcast
       Value *NewIncV = getOrCreateVectorValue(ScIncV, 0);
       NewPhi->addIncoming(NewIncV, NewPredBB);
     }
   }
 }
 
 void InnerLoopVectorizer::widenGEP(GetElementPtrInst *GEP, VPUser &Operands,
-                                   unsigned UF, unsigned VF,
+                                   unsigned UF, ElementCount VF,
                                    bool IsPtrLoopInvariant,
                                    SmallBitVector &IsIndexLoopInvariant,
                                    VPTransformState &State) {
   // Construct a vector GEP by widening the operands of the scalar GEP as
   // necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP
   // results in a vector of pointers when at least one operand of the GEP
   // is vector-typed. Thus, to keep the representation compact, we only use
   // vector-typed operands for loop-varying values.
 
-  if (VF > 1 && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) {
+  if (VF.isVector() && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) {
     // If we are vectorizing, but the GEP has only loop-invariant operands,
     // the GEP we build (by only using vector-typed operands for
     // loop-varying values) would be a scalar pointer. Thus, to ensure we
     // produce a vector of pointers, we need to either arbitrarily pick an
     // operand to broadcast, or broadcast a clone of the original GEP.
     // Here, we broadcast a clone of the original.
     //
     // TODO: If at some point we decide to scalarize instructions having
for (unsigned Part = 0; Part < UF; ++Part) {
              "NewGEP is not a pointer vector");
       VectorLoopValueMap.setVectorValue(GEP, Part, NewGEP);
       addMetadata(NewGEP, GEP);
     }
   }
 }
 
 void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, unsigned UF,
-                                              unsigned VF) {
+                                              ElementCount VF) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   PHINode *P = cast<PHINode>(PN);
   if (EnableVPlanNativePath) {
     // Currently we enter here in the VPlan-native path for non-induction
     // PHIs where all control flow is uniform. We simply widen these PHIs.
     // Create a vector phi with no operands - the vector phi operands will be
     // set at the end of vector code generation.
     Type *VecTy =
-        (VF == 1) ? PN->getType() : FixedVectorType::get(PN->getType(), VF);
+        (VF.isScalar()) ? PN->getType() : VectorType::get(PN->getType(), VF);
     Value *VecPhi = Builder.CreatePHI(VecTy, PN->getNumOperands(), "vec.phi");
     VectorLoopValueMap.setVectorValue(P, 0, VecPhi);
     OrigPHIsToFix.push_back(P);
 
     return;
   }
 
   assert(PN->getParent() == OrigLoop->getHeader() &&
          "Non-header phis should have been handled elsewhere");
 
   // In order to support recurrences we need to be able to vectorize Phi nodes.
   // Phi nodes have cycles, so we need to vectorize them in two stages. This is
   // stage #1: We create a new vector PHI node with no incoming edges. We'll use
   // this value when we vectorize all of the instructions that use the PHI.
   if (Legal->isReductionVariable(P) || Legal->isFirstOrderRecurrence(P)) {
     for (unsigned Part = 0; Part < UF; ++Part) {
       // This is phase one of vectorizing PHIs.
-      bool ScalarPHI = (VF == 1) || Cost->isInLoopReduction(cast<PHINode>(PN));
+      bool ScalarPHI =
+          (VF.isScalar()) || Cost->isInLoopReduction(cast<PHINode>(PN));
       Type *VecTy =
-          ScalarPHI ? PN->getType() : FixedVectorType::get(PN->getType(), VF);
+          ScalarPHI ? PN->getType() : VectorType::get(PN->getType(), VF);
       Value *EntryPart = PHINode::Create(
           VecTy, 2, "vec.phi", &*LoopVectorBody->getFirstInsertionPt());
       VectorLoopValueMap.setVectorValue(P, Part, EntryPart);
     }
     return;
   }
 
   setDebugLocFromInst(Builder, P);
case InductionDescriptor::IK_PtrInduction: {
 
     if (Cost->isScalarAfterVectorization(P, VF)) {
       // This is the normalized GEP that starts counting at zero.
       Value *PtrInd =
           Builder.CreateSExtOrTrunc(Induction, II.getStep()->getType());
       // Determine the number of scalars we need to generate for each unroll
       // iteration. If the instruction is uniform, we only need to generate the
       // first lane. Otherwise, we generate all VF values.
-      unsigned Lanes = Cost->isUniformAfterVectorization(P, VF) ? 1 : VF;
+      unsigned Lanes = Cost->isUniformAfterVectorization(P, VF) ? 1 : VF.Min;
       for (unsigned Part = 0; Part < UF; ++Part) {
         for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
-          Constant *Idx = ConstantInt::get(PtrInd->getType(), Lane + Part * VF);
+          Constant *Idx =
+              ConstantInt::get(PtrInd->getType(), Lane + Part * VF.Min);
           Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx);
           Value *SclrGep =
               emitTransformedIndex(Builder, GlobalIdx, PSE.getSE(), DL, II);
           SclrGep->setName("next.gep");
           VectorLoopValueMap.setScalarValue(P, {Part, Lane}, SclrGep);
         }
       }
       return;
case InductionDescriptor::IK_PtrInduction: {
     BasicBlock *LoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch();
     Instruction *InductionLoc = LoopLatch->getTerminator();
     const SCEV *ScalarStep = II.getStep();
     SCEVExpander Exp(*PSE.getSE(), DL, "induction");
     Value *ScalarStepValue =
         Exp.expandCodeFor(ScalarStep, PhiType, InductionLoc);
     Value *InductionGEP = GetElementPtrInst::Create(
         ScStValueType->getPointerElementType(), NewPointerPhi,
-        Builder.CreateMul(ScalarStepValue, ConstantInt::get(PhiType, VF * UF)),
+        Builder.CreateMul(ScalarStepValue,
+                          ConstantInt::get(PhiType, VF.Min * UF)),
         "ptr.ind", InductionLoc);
     NewPointerPhi->addIncoming(InductionGEP, LoopLatch);
 
     // Create UF many actual address geps that use the pointer
     // phi as base and a vectorized version of the step value
     // (<step*0, ..., step*N>) as offset.
     for (unsigned Part = 0; Part < UF; ++Part) {
       SmallVector<Constant *, 8> Indices;
       // Create a vector of consecutive numbers from zero to VF.
-      for (unsigned i = 0; i < VF; ++i)
-        Indices.push_back(ConstantInt::get(PhiType, i + Part * VF));
+      for (unsigned i = 0; i < VF.Min; ++i)
+        Indices.push_back(ConstantInt::get(PhiType, i + Part * VF.Min));
       Constant *StartOffset = ConstantVector::get(Indices);
 
       Value *GEP = Builder.CreateGEP(
           ScStValueType->getPointerElementType(), NewPointerPhi,
           Builder.CreateMul(StartOffset,
-                            Builder.CreateVectorSplat(VF, ScalarStepValue),
+                            Builder.CreateVectorSplat(VF.Min, ScalarStepValue),
                             "vector.gep"));
       VectorLoopValueMap.setVectorValue(P, Part, GEP);
     }
   }
   }
 }
 
 /// A helper function for checking whether an integer division-related
assert((I.getOpcode() == Instruction::UDiv ||
          "Unexpected instruction");
   Value *Divisor = I.getOperand(1);
   auto *CInt = dyn_cast<ConstantInt>(Divisor);
   return !CInt || CInt->isZero();
 }
 
 void InnerLoopVectorizer::widenInstruction(Instruction &I, VPUser &User,
                                            VPTransformState &State) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   switch (I.getOpcode()) {
   case Instruction::Call:
   case Instruction::Br:
   case Instruction::PHI:
   case Instruction::GetElementPtr:
   case Instruction::Select:
     llvm_unreachable("This instruction is handled by a different recipe.");
   case Instruction::UDiv:
void InnerLoopVectorizer::widenInstruction(Instruction &I, VPUser &User,
   case Instruction::UIToFP:
   case Instruction::Trunc:
   case Instruction::FPTrunc:
   case Instruction::BitCast: {
     auto *CI = cast<CastInst>(&I);
     setDebugLocFromInst(Builder, CI);
 
     /// Vectorize casts.
+    assert(!VF.Scalable && "VF is assumed to be non scalable.");
     Type *DestTy =
-        (VF == 1) ? CI->getType() : FixedVectorType::get(CI->getType(), VF);
+        (VF.isScalar()) ? CI->getType() : VectorType::get(CI->getType(), VF);
 
     for (unsigned Part = 0; Part < UF; ++Part) {
       Value *A = State.get(User.getOperand(0), Part);
       Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy);
       VectorLoopValueMap.setVectorValue(&I, Part, Cast);
       addMetadata(Cast, &I);
     }
     break;
assert(!isa<DbgInfoIntrinsic>(I) &&
          "DbgInfoIntrinsic should have been dropped during VPlan construction");
   setDebugLocFromInst(Builder, &I);
 
   Module *M = I.getParent()->getParent()->getParent();
   auto *CI = cast<CallInst>(&I);
 
   SmallVector<Type *, 4> Tys;
   for (Value *ArgOperand : CI->arg_operands())
-    Tys.push_back(ToVectorTy(ArgOperand->getType(), VF));
+    Tys.push_back(ToVectorTy(ArgOperand->getType(), VF.Min));
 
   Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
 
   // The flag shows whether we use Intrinsic or a usual Call for vectorized
   // version of the instruction.
   // Is it beneficial to perform intrinsic call compared to lib call?
   bool NeedToScalarize = false;
   unsigned CallCost = Cost->getVectorCallCost(CI, VF, NeedToScalarize);
for (auto &I : enumerate(ArgOperands.operands())) {
         Arg = State.get(I.value(), {0, 0});
       Args.push_back(Arg);
     }
 
     Function *VectorF;
     if (UseVectorIntrinsic) {
       // Use vector version of the intrinsic.
       Type *TysForDecl[] = {CI->getType()};
-      if (VF > 1)
-        TysForDecl[0] =
-            FixedVectorType::get(CI->getType()->getScalarType(), VF);
+      if (VF.isVector()) {
+        assert(!VF.Scalable && "VF is assumed to be non scalable.");
+        TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF);
+      }
       VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl);
       assert(VectorF && "Can't retrieve vector intrinsic.");
     } else {
       // Use vector version of the function call.
-      const VFShape Shape = VFShape::get(*CI, ElementCount::getFixed(VF),
-                                         false /*HasGlobalPred*/);
+      const VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/);
 #ifndef NDEBUG
       assert(VFDatabase(*CI).getVectorizedFunction(Shape) != nullptr &&
              "Can't create vector function.");
 #endif
         VectorF = VFDatabase(*CI).getVectorizedFunction(Shape);
     }
       SmallVector<OperandBundleDef, 1> OpBundles;
       CI->getOperandBundlesAsDefs(OpBundles);
for (unsigned Part = 0; Part < UF; ++Part) {
     Value *Op0 = State.get(Operands.getOperand(1), Part);
     Value *Op1 = State.get(Operands.getOperand(2), Part);
     Value *Sel = Builder.CreateSelect(Cond, Op0, Op1);
     VectorLoopValueMap.setVectorValue(&I, Part, Sel);
     addMetadata(Sel, &I);
   }
 }
 
-void LoopVectorizationCostModel::collectLoopScalars(unsigned VF) {
+void LoopVectorizationCostModel::collectLoopScalars(ElementCount VF) {
   // We should not collect Scalars more than once per VF. Right now, this
   // function is called from collectUniformsAndScalars(), which already does
   // this check. Collecting Scalars for VF=1 does not make any sense.
-  assert(VF >= 2 && Scalars.find(VF) == Scalars.end() &&
+  assert(VF.isVector() && Scalars.find(VF) == Scalars.end() &&
          "This function should not be visited twice for the same VF");
 
   SmallSetVector<Instruction *, 8> Worklist;
 
   // These sets are used to seed the analysis with pointers used by memory
   // accesses that will remain scalar.
   SmallSetVector<Instruction *, 8> ScalarPtrs;
   SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs;
for (auto &Induction : Legal->getInductionVars()) {
     LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n");
     LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate
                       << "\n");
   }
 
   Scalars[VF].insert(Worklist.begin(), Worklist.end());
 }
 
-bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I, unsigned VF) {
+bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I,
+                                                         ElementCount VF) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   if (!blockNeedsPredication(I->getParent()))
     return false;
   switch(I->getOpcode()) {
   default:
     break;
   case Instruction::Load:
   case Instruction::Store: {
     if (!Legal->isMaskRequired(I))
       return false;
     auto *Ptr = getLoadStorePointerOperand(I);
     auto *Ty = getMemInstValueType(I);
     // We have already decided how to vectorize this instruction, get that
     // result.
-    if (VF > 1) {
+    if (VF.isVector()) {
       InstWidening WideningDecision = getWideningDecision(I, VF);
       assert(WideningDecision != CM_Unknown &&
              "Widening decision should be ready at this moment");
       return WideningDecision == CM_Scalarize;
     }
     const Align Alignment = getLoadStoreAlignment(I);
     return isa<LoadInst>(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) ||
                                 isLegalMaskedGather(Ty, Alignment))
                             : !(isLegalMaskedStore(Ty, Ptr, Alignment) ||
                                 isLegalMaskedScatter(Ty, Alignment));
   }
   case Instruction::UDiv:
   case Instruction::SDiv:
   case Instruction::SRem:
   case Instruction::URem:
     return mayDivideByZero(*I);
   }
   return false;
 }
 
-bool LoopVectorizationCostModel::interleavedAccessCanBeWidened(Instruction *I,
-                                                               unsigned VF) {
+bool LoopVectorizationCostModel::interleavedAccessCanBeWidened(
+    Instruction *I, ElementCount VF) {
   assert(isAccessInterleaved(I) && "Expecting interleaved access.");
   assert(getWideningDecision(I, VF) == CM_Unknown &&
          "Decision should not be set yet.");
   auto *Group = getInterleavedAccessGroup(I);
   assert(Group && "Must have a group.");
 
   // If the instruction's allocated size doesn't equal it's type size, it
   // requires padding and will be scalarized.
assert(useMaskedInterleavedAccesses(TTI) &&
          "Masked interleave-groups for predicated accesses are not enabled.");
 
   auto *Ty = getMemInstValueType(I);
   const Align Alignment = getLoadStoreAlignment(I);
   return isa<LoadInst>(I) ? TTI.isLegalMaskedLoad(Ty, Alignment)
                           : TTI.isLegalMaskedStore(Ty, Alignment);
 }
 
-bool LoopVectorizationCostModel::memoryInstructionCanBeWidened(Instruction *I,
-                                                               unsigned VF) {
+bool LoopVectorizationCostModel::memoryInstructionCanBeWidened(
+    Instruction *I, ElementCount VF) {
   // Get and ensure we have a valid memory instruction.
   LoadInst *LI = dyn_cast<LoadInst>(I);
   StoreInst *SI = dyn_cast<StoreInst>(I);
   assert((LI || SI) && "Invalid memory instruction");
 
   auto *Ptr = getLoadStorePointerOperand(I);
 
   // In order to be widened, the pointer should be consecutive, first of all.
bool LoopVectorizationCostModel::memoryInstructionCanBeWidened(
   auto &DL = I->getModule()->getDataLayout();
   auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType();
   if (hasIrregularType(ScalarTy, DL, VF))
     return false;
 
   return true;
 }
 
-void LoopVectorizationCostModel::collectLoopUniforms(unsigned VF) {
+void LoopVectorizationCostModel::collectLoopUniforms(ElementCount VF) {
   // We should not collect Uniforms more than once per VF. Right now,
   // this function is called from collectUniformsAndScalars(), which
   // already does this check. Collecting Uniforms for VF=1 does not make any
   // sense.
 
-  assert(VF >= 2 && Uniforms.find(VF) == Uniforms.end() &&
+  assert(VF.isVector() && Uniforms.find(VF) == Uniforms.end() &&
          "This function should not be visited twice for the same VF");
 
   // Visit the list of Uniforms. If we'll not find any uniform value, we'll
   // not analyze again.  Uniforms.count(VF) will return 1.
   Uniforms[VF].clear();
 
   // We now know that the loop is vectorizable!
   // Collect instructions inside the loop that will remain uniform after
void LoopVectorizationCostModel::collectLoopUniforms(ElementCount VF) {
   // are pointers that are treated like consecutive pointers during
   // vectorization. The pointer operands of interleaved accesses are an
   // example.
   SmallSetVector<Instruction *, 8> ConsecutiveLikePtrs;
 
   // Holds pointer operands of instructions that are possibly non-uniform.
   SmallPtrSet<Instruction *, 8> PossibleNonUniformPtrs;
 
-  auto isUniformDecision = [&](Instruction *I, unsigned VF) {
+  auto isUniformDecision = [&](Instruction *I, ElementCount VF) {
     InstWidening WideningDecision = getWideningDecision(I, VF);
     assert(WideningDecision != CM_Unknown &&
            "Widening decision should be ready at this moment");
 
     return (WideningDecision == CM_Widen ||
             WideningDecision == CM_Widen_Reverse ||
             WideningDecision == CM_Interleave);
   };
if (MaxVectorSize == 0) {
     return MaxVectorSize;
   }
 
   unsigned MaxVF = MaxVectorSize;
   if (TTI.shouldMaximizeVectorBandwidth(!isScalarEpilogueAllowed()) ||
       (MaximizeBandwidth && isScalarEpilogueAllowed())) {
     // Collect all viable vectorization factors larger than the default MaxVF
     // (i.e. MaxVectorSize).
-    SmallVector<unsigned, 8> VFs;
+    SmallVector<ElementCount, 8> VFs;
     unsigned NewMaxVectorSize = WidestRegister / SmallestType;
     for (unsigned VS = MaxVectorSize * 2; VS <= NewMaxVectorSize; VS *= 2)
-      VFs.push_back(VS);
+      VFs.push_back(ElementCount::getFixed(VS));
 
     // For each VF calculate its register usage.
     auto RUs = calculateRegisterUsage(VFs);
 
     // Select the largest VF which doesn't require more registers than existing
     // ones.
     for (int i = RUs.size() - 1; i >= 0; --i) {
       bool Selected = true;
       for (auto& pair : RUs[i].MaxLocalUsers) {
         unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first);
         if (pair.second > TargetNumRegisters)
           Selected = false;
       }
       if (Selected) {
-        MaxVF = VFs[i];
+        MaxVF = VFs[i].Min;
         break;
       }
     }
     if (unsigned MinVF = TTI.getMinimumVF(SmallestType)) {
       if (MaxVF < MinVF) {
         LLVM_DEBUG(dbgs() << "LV: Overriding calculated MaxVF(" << MaxVF
                           << ") with target's minimum: " << MinVF << '\n');
         MaxVF = MinVF;
       }
     }
   }
   return MaxVF;
 }
 
 VectorizationFactor
 LoopVectorizationCostModel::selectVectorizationFactor(unsigned MaxVF) {
-  float Cost = expectedCost(1).first;
+  float Cost = expectedCost(ElementCount::getFixed(1)).first;
   const float ScalarCost = Cost;
   unsigned Width = 1;
   LLVM_DEBUG(dbgs() << "LV: Scalar loop costs: " << (int)ScalarCost << ".\n");
 
   bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled;
   if (ForceVectorization && MaxVF > 1) {
     // Ignore scalar width, because the user explicitly wants vectorization.
     // Initialize cost to max so that VF = 2 is, at least, chosen during cost
     // evaluation.
     Cost = std::numeric_limits<float>::max();
   }
 
   for (unsigned i = 2; i <= MaxVF; i *= 2) {
     // Notice that the vector loop needs to be executed less times, so
     // we need to divide the cost of the vector loops by the width of
     // the vector elements.
-    VectorizationCostTy C = expectedCost(i);
+    VectorizationCostTy C = expectedCost(ElementCount::getFixed(i));
     float VectorCost = C.first / (float)i;
     LLVM_DEBUG(dbgs() << "LV: Vector loop of width " << i
                       << " costs: " << (int)VectorCost << ".\n");
     if (!C.second && !ForceVectorization) {
       LLVM_DEBUG(
           dbgs() << "LV: Not considering vector loop of width " << i
                  << " because it will not generate any vector instructions.\n");
       continue;
if (!EnableCondStoresVectorization && NumPredStores) {
     Width = 1;
     Cost = ScalarCost;
   }
 
   LLVM_DEBUG(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs()
              << "LV: Vectorization seems to be not beneficial, "
              << "but was forced by a user.\n");
   LLVM_DEBUG(dbgs() << "LV: Selecting VF: " << Width << ".\n");
-  VectorizationFactor Factor = {Width, (unsigned)(Width * Cost)};
+  VectorizationFactor Factor = {ElementCount::getFixed(Width),
+                                (unsigned)(Width * Cost)};
   return Factor;
 }
 
 std::pair<unsigned, unsigned>
 LoopVectorizationCostModel::getSmallestAndWidestTypes() {
   unsigned MinWidth = -1U;
   unsigned MaxWidth = 8;
   const DataLayout &DL = TheFunction->getParent()->getDataLayout();
for (Instruction &I : BB->instructionsWithoutDebug()) {
       MaxWidth = std::max(MaxWidth,
                           (unsigned)DL.getTypeSizeInBits(T->getScalarType()));
     }
   }
 
   return {MinWidth, MaxWidth};
 }
 
-unsigned LoopVectorizationCostModel::selectInterleaveCount(unsigned VF,
+unsigned LoopVectorizationCostModel::selectInterleaveCount(ElementCount VF,
                                                            unsigned LoopCost) {
   // -- The interleave heuristics --
   // We interleave the loop in order to expose ILP and reduce the loop overhead.
   // There are many micro-architectural considerations that we can't predict
   // at this level. For example, frontend pressure (on decode or fetch) due to
   // code size, or the number and capabilities of the execution ports.
   //
   // We use the following heuristics to select the interleave count:
if (EnableIndVarRegisterHeur) {
           PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs - 1) /
                         std::max(1U, (MaxLocalUsers - 1)));
     }
 
     IC = std::min(IC, TmpIC);
   }
 
   // Clamp the interleave ranges to reasonable counts.
-  unsigned MaxInterleaveCount = TTI.getMaxInterleaveFactor(VF);
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
+  unsigned MaxInterleaveCount = TTI.getMaxInterleaveFactor(VF.Min);
 
   // Check if the user has overridden the max.
   if (VF == 1) {
     if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
       MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor;
   } else {
     if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
       MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor;
   }
 
   // If trip count is known or estimated compile time constant, limit the
   // interleave count to be less than the trip count divided by VF.
   if (BestKnownTC) {
-    MaxInterleaveCount = std::min(*BestKnownTC / VF, MaxInterleaveCount);
+    MaxInterleaveCount = std::min(*BestKnownTC / VF.Min, MaxInterleaveCount);
   }
 
   // If we did not calculate the cost for VF (because the user selected the VF)
   // then we calculate the cost of VF here.
   if (LoopCost == 0)
     LoopCost = expectedCost(VF).first;
 
   assert(LoopCost && "Non-zero loop cost expected");
 
   // Clamp the calculated IC to be between the 1 and the max interleave count
   // that the target and trip count allows.
   if (IC > MaxInterleaveCount)
     IC = MaxInterleaveCount;
   else if (IC < 1)
     IC = 1;
 
   // Interleave if we vectorized this loop and there is a reduction that could
   // benefit from interleaving.
-  if (VF > 1 && !Legal->getReductionVars().empty()) {
+  if (VF.isVector() && !Legal->getReductionVars().empty()) {
     LLVM_DEBUG(dbgs() << "LV: Interleaving because of reductions.\n");
     return IC;
   }
 
   // Note that if we've already vectorized the loop we will have done the
   // runtime check and so interleaving won't require further checks.
   bool InterleavingRequiresRuntimePointerCheck =
-      (VF == 1 && Legal->getRuntimePointerChecking()->Need);
+      (VF.isScalar() && Legal->getRuntimePointerChecking()->Need);
 
   // We want to interleave small loops in order to reduce the loop overhead and
   // potentially expose ILP opportunities.
   LLVM_DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n');
   if (!InterleavingRequiresRuntimePointerCheck && LoopCost < SmallLoopCost) {
     // We assume that the cost overhead is 1 and we use the cost model
     // to estimate the cost of the loop and interleave until the cost of the
     // loop overhead is about 5% of the cost of the loop.
if (TTI.enableAggressiveInterleaving(HasReductions)) {
     return IC;
   }
 
   LLVM_DEBUG(dbgs() << "LV: Not Interleaving.\n");
   return 1;
 }
 
 SmallVector<LoopVectorizationCostModel::RegisterUsage, 8>
-LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<unsigned> VFs) {
+LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<ElementCount> VFs) {
   // This function calculates the register usage by measuring the highest number
   // of values that are alive at a single location. Obviously, this is a very
   // rough estimation. We scan the loop in a topological order in order and
   // assign a number to each instruction. We use RPO to ensure that defs are
   // met before their users. We assume that each instruction that has in-loop
   // users starts an interval. We record every time that an in-loop value is
   // used, so we have a list of the first and last occurrences of each
   // instruction. Next, we transpose this data structure into a multi map that
LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<ElementCount> VFs) {
   const DataLayout &DL = TheFunction->getParent()->getDataLayout();
 
   SmallVector<RegisterUsage, 8> RUs(VFs.size());
   SmallVector<SmallMapVector<unsigned, unsigned, 4>, 8> MaxUsages(VFs.size());
 
   LLVM_DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
 
   // A lambda that gets the register usage for the given type and VF.
-  auto GetRegUsage = [&DL, WidestRegister](Type *Ty, unsigned VF) {
+  auto GetRegUsage = [&DL, WidestRegister](Type *Ty, ElementCount VF) {
     if (Ty->isTokenTy())
       return 0U;
     unsigned TypeSize = DL.getTypeSizeInBits(Ty->getScalarType());
-    return std::max<unsigned>(1, VF * TypeSize / WidestRegister);
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
+    return std::max<unsigned>(1, VF.Min * TypeSize / WidestRegister);
   };
 
   for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) {
     Instruction *I = IdxToInstr[i];
 
     // Remove all of the instructions that end at this location.
     InstrList &List = TransposeEnds[i];
     for (Instruction *ToRemove : List)
       OpenIntervals.erase(ToRemove);
 
     // Ignore instructions that are never used within the loop.
     if (!Ends.count(I))
       continue;
 
     // Skip ignored values.
     if (ValuesToIgnore.count(I))
       continue;
 
     // For each VF find the maximum usage of registers.
     for (unsigned j = 0, e = VFs.size(); j < e; ++j) {
       // Count the number of live intervals.
       SmallMapVector<unsigned, unsigned, 4> RegUsage;
 
-      if (VFs[j] == 1) {
+      if (VFs[j].isScalar()) {
         for (auto Inst : OpenIntervals) {
           unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType());
           if (RegUsage.find(ClassID) == RegUsage.end())
             RegUsage[ClassID] = 1;
           else
             RegUsage[ClassID] += 1;
         }
       } else {
for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) {
     // Add the current instruction to the list of open intervals.
     OpenIntervals.insert(I);
   }
 
   for (unsigned i = 0, e = VFs.size(); i < e; ++i) {
     SmallMapVector<unsigned, unsigned, 4> Invariant;
   
     for (auto Inst : LoopInvariants) {
-      unsigned Usage = VFs[i] == 1 ? 1 : GetRegUsage(Inst->getType(), VFs[i]);
-      unsigned ClassID = TTI.getRegisterClassForType(VFs[i] > 1, Inst->getType());
+      unsigned Usage =
+          VFs[i].isScalar() ? 1 : GetRegUsage(Inst->getType(), VFs[i]);
+      unsigned ClassID =
+          TTI.getRegisterClassForType(VFs[i].isVector(), Inst->getType());
       if (Invariant.find(ClassID) == Invariant.end())
         Invariant[ClassID] = Usage;
       else
         Invariant[ClassID] += Usage;
     }
 
     LLVM_DEBUG({
       dbgs() << "LV(REG): VF = " << VFs[i] << '\n';
bool LoopVectorizationCostModel::useEmulatedMaskMemRefHack(Instruction *I){
   // Masked Load/Gather emulation was previously never allowed.
   // Limited number of Masked Store/Scatter emulation was allowed.
   assert(isPredicatedInst(I) && "Expecting a scalar emulated instruction");
   return isa<LoadInst>(I) ||
          (isa<StoreInst>(I) &&
           NumPredStores > NumberOfStoresToPredicate);
 }
 
-void LoopVectorizationCostModel::collectInstsToScalarize(unsigned VF) {
+void LoopVectorizationCostModel::collectInstsToScalarize(ElementCount VF) {
   // If we aren't vectorizing the loop, or if we've already collected the
   // instructions to scalarize, there's nothing to do. Collection may already
   // have occurred if we have a user-selected VF and are now computing the
   // expected cost for interleaving.
-  if (VF < 2 || InstsToScalarize.find(VF) != InstsToScalarize.end())
+  if (VF.isScalar() || VF.isZero() ||
+      InstsToScalarize.find(VF) != InstsToScalarize.end())
     return;
 
   // Initialize a mapping for VF in InstsToScalalarize. If we find that it's
   // not profitable to scalarize any instructions, the presence of VF in the
   // map will indicate that we've analyzed it already.
   ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF];
 
   // Find all the instructions that are scalar with predication in the loop and
for (Instruction &I : *BB)
         // Remember that BB will remain after vectorization.
         PredicatedBBsAfterVectorization.insert(BB);
       }
   }
 }
 
 int LoopVectorizationCostModel::computePredInstDiscount(
     Instruction *PredInst, DenseMap<Instruction *, unsigned> &ScalarCosts,
-    unsigned VF) {
+    ElementCount VF) {
   assert(!isUniformAfterVectorization(PredInst, VF) &&
          "Instruction marked uniform-after-vectorization will be predicated");
 
   // Initialize the discount to zero, meaning that the scalar version and the
   // vector version cost the same.
   int Discount = 0;
 
   // Holds instructions to analyze. The instructions we visit are mapped in
while (!Worklist.empty()) {
     // Compute the cost of the vector instruction. Note that this cost already
     // includes the scalarization overhead of the predicated instruction.
     unsigned VectorCost = getInstructionCost(I, VF).first;
 
     // Compute the cost of the scalarized instruction. This cost is the cost of
     // the instruction as if it wasn't if-converted and instead remained in the
     // predicated block. We will scale this cost by block probability after
     // computing the scalarization overhead.
-    unsigned ScalarCost = VF * getInstructionCost(I, 1).first;
+    assert(!VF.Scalable && "scalable vectors not yet supported.");
+    unsigned ScalarCost =
+        VF.Min * getInstructionCost(I, ElementCount::getFixed(1)).first;
 
     // Compute the scalarization overhead of needed insertelement instructions
     // and phi nodes.
     if (isScalarWithPredication(I) && !I->getType()->isVoidTy()) {
       ScalarCost += TTI.getScalarizationOverhead(
           cast<VectorType>(ToVectorTy(I->getType(), VF)),
-          APInt::getAllOnesValue(VF), true, false);
-      ScalarCost += VF * TTI.getCFInstrCost(Instruction::PHI,
-                                            TTI::TCK_RecipThroughput);
+          APInt::getAllOnesValue(VF.Min), true, false);
+      assert(!VF.Scalable && "scalable vectors not yet supported.");
+      ScalarCost +=
+          VF.Min *
+          TTI.getCFInstrCost(Instruction::PHI, TTI::TCK_RecipThroughput);
     }
 
     // Compute the scalarization overhead of needed extractelement
     // instructions. For each of the instruction's operands, if the operand can
     // be scalarized, add it to the worklist; otherwise, account for the
     // overhead.
     for (Use &U : I->operands())
       if (auto *J = dyn_cast<Instruction>(U.get())) {
         assert(VectorType::isValidElementType(J->getType()) &&
                "Instruction has non-scalar type");
         if (canBeScalarized(J))
           Worklist.push_back(J);
-        else if (needsExtract(J, VF))
+        else if (needsExtract(J, VF)) {
+          assert(!VF.Scalable && "scalable vectors not yet supported.");
           ScalarCost += TTI.getScalarizationOverhead(
               cast<VectorType>(ToVectorTy(J->getType(), VF)),
-              APInt::getAllOnesValue(VF), false, true);
+              APInt::getAllOnesValue(VF.Min), false, true);
+        }
       }
 
     // Scale the total scalar cost by block probability.
     ScalarCost /= getReciprocalPredBlockProb();
 
     // Compute the discount. A non-negative discount means the vector version
     // of the instruction costs more, and scalarizing would be beneficial.
     Discount += VectorCost - ScalarCost;
     ScalarCosts[I] = ScalarCost;
   }
 
   return Discount;
 }
 
 LoopVectorizationCostModel::VectorizationCostTy
-LoopVectorizationCostModel::expectedCost(unsigned VF) {
+LoopVectorizationCostModel::expectedCost(ElementCount VF) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   VectorizationCostTy Cost;
 
   // For each block.
   for (BasicBlock *BB : TheLoop->blocks()) {
     VectorizationCostTy BlockCost;
 
     // For each instruction in the old loop.
     for (Instruction &I : BB->instructionsWithoutDebug()) {
       // Skip ignored values.
-      if (ValuesToIgnore.count(&I) || (VF > 1 && VecValuesToIgnore.count(&I)))
+      if (ValuesToIgnore.count(&I) ||
+          (VF.isVector() && VecValuesToIgnore.count(&I)))
         continue;
 
       VectorizationCostTy C = getInstructionCost(&I, VF);
 
       // Check if we should override the cost.
       if (ForceTargetInstructionCost.getNumOccurrences() > 0)
         C.first = ForceTargetInstructionCost;
 
       BlockCost.first += C.first;
       BlockCost.second |= C.second;
       LLVM_DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first
                         << " for VF " << VF << " For instruction: " << I
                         << '\n');
     }
 
     // If we are vectorizing a predicated block, it will have been
     // if-converted. This means that the block's instructions (aside from
     // stores and instructions that may divide by zero) will now be
     // unconditionally executed. For the scalar case, we may not always execute
     // the predicated block. Thus, scale the block's cost by the probability of
     // executing it.
-    if (VF == 1 && blockNeedsPredication(BB))
+    if (VF.isScalar() && blockNeedsPredication(BB))
       BlockCost.first /= getReciprocalPredBlockProb();
 
     Cost.first += BlockCost.first;
     Cost.second |= BlockCost.second;
   }
 
   return Cost;
 }
static const SCEV *getAddressAccessSCEV(
   return PSE.getSCEV(Ptr);
 }
 
 static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) {
   return Legal->hasStride(I->getOperand(0)) ||
          Legal->hasStride(I->getOperand(1));
 }
 
-unsigned LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I,
-                                                                 unsigned VF) {
-  assert(VF > 1 && "Scalarization cost of instruction implies vectorization.");
+unsigned
+LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I,
+                                                        ElementCount VF) {
+  assert(VF.isVector() &&
+         "Scalarization cost of instruction implies vectorization.");
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
   Type *ValTy = getMemInstValueType(I);
   auto SE = PSE.getSE();
 
   unsigned AS = getLoadStoreAddressSpace(I);
   Value *Ptr = getLoadStorePointerOperand(I);
   Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
 
   // Figure out whether the access is strided and get the stride value
   // if it's known in compile time
   const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, Legal, PSE, TheLoop);
 
   // Get the cost of the scalar memory instruction and address computation.
-  unsigned Cost = VF * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV);
+  unsigned Cost = VF.Min * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV);
 
   // Don't pass *I here, since it is scalar but will actually be part of a
   // vectorized loop where the user of it is a vectorized instruction.
   const Align Alignment = getLoadStoreAlignment(I);
-  Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(),
-                                   Alignment, AS, 
-                                   TTI::TCK_RecipThroughput);
+  Cost += VF.Min *
+          TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment,
+                              AS, TTI::TCK_RecipThroughput);
 
   // Get the overhead of the extractelement and insertelement instructions
   // we might create due to scalarization.
   Cost += getScalarizationOverhead(I, VF);
 
   // If we have a predicated store, it may not be executed for each vector
   // lane. Scale the cost by the probability of executing the predicated
   // block.
   if (isPredicatedInst(I)) {
     Cost /= getReciprocalPredBlockProb();
 
     if (useEmulatedMaskMemRefHack(I))
       // Artificially setting to a high enough value to practically disable
       // vectorization with such operations.
       Cost = 3000000;
   }
 
   return Cost;
 }
 
 unsigned LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I,
-                                                             unsigned VF) {
+                                                             ElementCount VF) {
   Type *ValTy = getMemInstValueType(I);
   auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
   Value *Ptr = getLoadStorePointerOperand(I);
   unsigned AS = getLoadStoreAddressSpace(I);
   int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
   enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
 
   assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
unsigned LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I,
 
   bool Reverse = ConsecutiveStride < 0;
   if (Reverse)
     Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
   return Cost;
 }
 
 unsigned LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I,
-                                                         unsigned VF) {
+                                                         ElementCount VF) {
   Type *ValTy = getMemInstValueType(I);
   auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
   const Align Alignment = getLoadStoreAlignment(I);
   unsigned AS = getLoadStoreAddressSpace(I);
   enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
   if (isa<LoadInst>(I)) {
     return TTI.getAddressComputationCost(ValTy) +
            TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS,
                                CostKind) +
            TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy);
   }
   StoreInst *SI = cast<StoreInst>(I);
 
   bool isLoopInvariantStoreValue = Legal->isUniform(SI->getValueOperand());
   return TTI.getAddressComputationCost(ValTy) +
          TTI.getMemoryOpCost(Instruction::Store, ValTy, Alignment, AS,
                              CostKind) +
-         (isLoopInvariantStoreValue
-              ? 0
-              : TTI.getVectorInstrCost(Instruction::ExtractElement, VectorTy,
-                                       VF - 1));
+         (isLoopInvariantStoreValue ? 0 : TTI.getVectorInstrCost(
+                                              Instruction::ExtractElement,
+                                              VectorTy, VF.Min - 1));
 }
 
 unsigned LoopVectorizationCostModel::getGatherScatterCost(Instruction *I,
-                                                          unsigned VF) {
+                                                          ElementCount VF) {
   Type *ValTy = getMemInstValueType(I);
   auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
   const Align Alignment = getLoadStoreAlignment(I);
   const Value *Ptr = getLoadStorePointerOperand(I);
 
   return TTI.getAddressComputationCost(VectorTy) +
          TTI.getGatherScatterOpCost(
              I->getOpcode(), VectorTy, Ptr, Legal->isMaskRequired(I), Alignment,
              TargetTransformInfo::TCK_RecipThroughput, I);
 }
 
 unsigned LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I,
-                                                            unsigned VF) {
+                                                            ElementCount VF) {
   Type *ValTy = getMemInstValueType(I);
   auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
   unsigned AS = getLoadStoreAddressSpace(I);
 
   auto Group = getInterleavedAccessGroup(I);
   assert(Group && "Fail to get an interleaved access group.");
 
   unsigned InterleaveFactor = Group->getFactor();
-  auto *WideVecTy = FixedVectorType::get(ValTy, VF * InterleaveFactor);
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
+  auto *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor);
 
   // Holds the indices of existing members in an interleaved load group.
   // An interleaved store group doesn't need this as it doesn't allow gaps.
   SmallVector<unsigned, 4> Indices;
   if (isa<LoadInst>(I)) {
     for (unsigned i = 0; i < InterleaveFactor; i++)
       if (Group->getMember(i))
         Indices.push_back(i);
assert(!Legal->isMaskRequired(I) &&
            "Reverse masked interleaved access not supported.");
     Cost += Group->getNumMembers() *
             TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
   }
   return Cost;
 }
 
 unsigned LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I,
-                                                              unsigned VF) {
+                                                              ElementCount VF) {
   // Calculate scalar cost only. Vectorization cost should be ready at this
   // moment.
-  if (VF == 1) {
+  if (VF.isScalar()) {
     Type *ValTy = getMemInstValueType(I);
     const Align Alignment = getLoadStoreAlignment(I);
     unsigned AS = getLoadStoreAddressSpace(I);
 
     return TTI.getAddressComputationCost(ValTy) +
            TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS,
                                TTI::TCK_RecipThroughput, I);
   }
   return getWideningCost(I, VF);
 }
 
 LoopVectorizationCostModel::VectorizationCostTy
-LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
+LoopVectorizationCostModel::getInstructionCost(Instruction *I,
+                                               ElementCount VF) {
+  assert(!VF.Scalable &&
+         "the cost model is not yet implemented for scalable vectorization");
   // If we know that this instruction will remain uniform, check the cost of
   // the scalar version.
   if (isUniformAfterVectorization(I, VF))
-    VF = 1;
+    VF = ElementCount::getFixed(1);
 
-  if (VF > 1 && isProfitableToScalarize(I, VF))
+  if (VF.isVector() && isProfitableToScalarize(I, VF))
     return VectorizationCostTy(InstsToScalarize[VF][I], false);
 
   // Forced scalars do not have any scalarization overhead.
   auto ForcedScalar = ForcedScalars.find(VF);
-  if (VF > 1 && ForcedScalar != ForcedScalars.end()) {
+  if (VF.isVector() && ForcedScalar != ForcedScalars.end()) {
     auto InstSet = ForcedScalar->second;
     if (InstSet.count(I))
-      return VectorizationCostTy((getInstructionCost(I, 1).first * VF), false);
+      return VectorizationCostTy(
+          (getInstructionCost(I, ElementCount::getFixed(1)).first * VF.Min),
+          false);
   }
 
   Type *VectorTy;
   unsigned C = getInstructionCost(I, VF, VectorTy);
 
-  bool TypeNotScalarized =
-      VF > 1 && VectorTy->isVectorTy() && TTI.getNumberOfParts(VectorTy) < VF;
+  bool TypeNotScalarized = VF.isVector() && VectorTy->isVectorTy() &&
+                           TTI.getNumberOfParts(VectorTy) < VF.Min;
   return VectorizationCostTy(C, TypeNotScalarized);
 }
 
 unsigned LoopVectorizationCostModel::getScalarizationOverhead(Instruction *I,
-                                                              unsigned VF) {
+                                                              ElementCount VF) {
 
-  if (VF == 1)
+  assert(!VF.Scalable &&
+         "cannot compute scalarization overhead for scalable vectorization");
+  if (VF.isScalar())
     return 0;
 
   unsigned Cost = 0;
   Type *RetTy = ToVectorTy(I->getType(), VF);
   if (!RetTy->isVoidTy() &&
       (!isa<LoadInst>(I) || !TTI.supportsEfficientVectorElementLoadStore()))
     Cost += TTI.getScalarizationOverhead(
-        cast<VectorType>(RetTy), APInt::getAllOnesValue(VF), true, false);
+        cast<VectorType>(RetTy), APInt::getAllOnesValue(VF.Min), true, false);
 
   // Some targets keep addresses scalar.
   if (isa<LoadInst>(I) && !TTI.prefersVectorizedAddressing())
     return Cost;
 
   // Some targets support efficient element stores.
   if (isa<StoreInst>(I) && TTI.supportsEfficientVectorElementLoadStore())
     return Cost;
 
   // Collect operands to consider.
   CallInst *CI = dyn_cast<CallInst>(I);
   Instruction::op_range Ops = CI ? CI->arg_operands() : I->operands();
 
   // Skip operands that do not require extraction/scalarization and do not incur
   // any overhead.
-  return Cost + TTI.getOperandsScalarizationOverhead(
-                    filterExtractingOperands(Ops, VF), VF);
+  return Cost +
+         TTI.getOperandsScalarizationOverhead(filterExtractingOperands(Ops, VF),
+                                              VF.Min);
 }
 
-void LoopVectorizationCostModel::setCostBasedWideningDecision(unsigned VF) {
-  if (VF == 1)
+void LoopVectorizationCostModel::setCostBasedWideningDecision(ElementCount VF) {
+  assert(!VF.Scalable && "scalable vectors not yet supported.");
+  if (VF.isScalar())
     return;
   NumPredStores = 0;
   for (BasicBlock *BB : TheLoop->blocks()) {
     // For each instruction in the old loop.
     for (Instruction &I : *BB) {
       Value *Ptr =  getLoadStorePointerOperand(&I);
       if (!Ptr)
         continue;
for (auto *I : AddrDefs) {
     if (isa<LoadInst>(I)) {
       // Setting the desired widening decision should ideally be handled in
       // by cost functions, but since this involves the task of finding out
       // if the loaded register is involved in an address computation, it is
       // instead changed here when we know this is the case.
       InstWidening Decision = getWideningDecision(I, VF);
       if (Decision == CM_Widen || Decision == CM_Widen_Reverse)
         // Scalarize a widened load of address.
-        setWideningDecision(I, VF, CM_Scalarize,
-                            (VF * getMemoryInstructionCost(I, 1)));
+        setWideningDecision(
+            I, VF, CM_Scalarize,
+            (VF.Min * getMemoryInstructionCost(I, ElementCount::getFixed(1))));
       else if (auto Group = getInterleavedAccessGroup(I)) {
         // Scalarize an interleave group of address loads.
         for (unsigned I = 0; I < Group->getFactor(); ++I) {
           if (Instruction *Member = Group->getMember(I))
-            setWideningDecision(Member, VF, CM_Scalarize,
-                                (VF * getMemoryInstructionCost(Member, 1)));
+            setWideningDecision(
+                Member, VF, CM_Scalarize,
+                (VF.Min *
+                 getMemoryInstructionCost(Member, ElementCount::getFixed(1))));
         }
       }
     } else
       // Make sure I gets scalarized and a cost estimate without
       // scalarization overhead.
       ForcedScalars[VF].insert(I);
   }
 }
 
 unsigned LoopVectorizationCostModel::getInstructionCost(Instruction *I,
-                                                        unsigned VF,
+                                                        ElementCount VF,
                                                         Type *&VectorTy) {
   Type *RetTy = I->getType();
   if (canTruncateToMinimalBitwidth(I, VF))
     RetTy = IntegerType::get(RetTy->getContext(), MinBWs[I]);
   VectorTy = isScalarAfterVectorization(I, VF) ? RetTy : ToVectorTy(RetTy, VF);
   auto SE = PSE.getSE();
   TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
 
   // TODO: We need to estimate the cost of intrinsic calls.
   switch (I->getOpcode()) {
   case Instruction::GetElementPtr:
     // We mark this instruction as zero-cost because the cost of GEPs in
     // vectorized code depends on whether the corresponding memory instruction
     // is scalarized or not. Therefore, we handle GEPs with the memory
     // instruction cost.
     return 0;
   case Instruction::Br: {
     // In cases of scalarized and predicated instructions, there will be VF
     // predicated blocks in the vectorized loop. Each branch around these
     // blocks requires also an extract of its vector compare i1 element.
     bool ScalarPredicatedBB = false;
     BranchInst *BI = cast<BranchInst>(I);
-    if (VF > 1 && BI->isConditional() &&
+    if (VF.isVector() && BI->isConditional() &&
         (PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) ||
          PredicatedBBsAfterVectorization.count(BI->getSuccessor(1))))
       ScalarPredicatedBB = true;
 
     if (ScalarPredicatedBB) {
       // Return cost for branches around scalarized and predicated blocks.
+      assert(!VF.Scalable && "scalable vectors not yet supported.");
       auto *Vec_i1Ty =
-          FixedVectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
-      return (TTI.getScalarizationOverhead(Vec_i1Ty, APInt::getAllOnesValue(VF),
-                                           false, true) +
-              (TTI.getCFInstrCost(Instruction::Br, CostKind) * VF));
-    } else if (I->getParent() == TheLoop->getLoopLatch() || VF == 1)
+          VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
+      return (TTI.getScalarizationOverhead(
+                  Vec_i1Ty, APInt::getAllOnesValue(VF.Min), false, true) +
+              (TTI.getCFInstrCost(Instruction::Br, CostKind) * VF.Min));
+    } else if (I->getParent() == TheLoop->getLoopLatch() || VF.isScalar())
       // The back-edge branch will remain, as will all scalar branches.
       return TTI.getCFInstrCost(Instruction::Br, CostKind);
     else
       // This branch will be eliminated by if-conversion.
       return 0;
     // Note: We currently assume zero cost for an unconditional branch inside
     // a predicated block since it will become a fall-through, although we
     // may decide in the future to call TTI for all branches.
   }
   case Instruction::PHI: {
     auto *Phi = cast<PHINode>(I);
 
     // First-order recurrences are replaced by vector shuffles inside the loop.
     // NOTE: Don't use ToVectorTy as SK_ExtractSubvector expects a vector type.
-    if (VF > 1 && Legal->isFirstOrderRecurrence(Phi))
+    if (VF.isVector() && Legal->isFirstOrderRecurrence(Phi))
       return TTI.getShuffleCost(TargetTransformInfo::SK_ExtractSubvector,
-                                cast<VectorType>(VectorTy), VF - 1,
+                                cast<VectorType>(VectorTy), VF.Min - 1,
                                 FixedVectorType::get(RetTy, 1));
 
     // Phi nodes in non-header blocks (not inductions, reductions, etc.) are
     // converted into select instructions. We require N - 1 selects per phi
     // node, where N is the number of incoming values.
-    if (VF > 1 && Phi->getParent() != TheLoop->getHeader())
+    if (VF.isVector() && Phi->getParent() != TheLoop->getHeader())
       return (Phi->getNumIncomingValues() - 1) *
              TTI.getCmpSelInstrCost(
                  Instruction::Select, ToVectorTy(Phi->getType(), VF),
                  ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF),
                  CostKind);
 
     return TTI.getCFInstrCost(Instruction::PHI, CostKind);
   }
   case Instruction::UDiv:
   case Instruction::SDiv:
   case Instruction::URem:
   case Instruction::SRem:
     // If we have a predicated instruction, it may not be executed for each
     // vector lane. Get the scalarization cost and scale this amount by the
     // probability of executing the predicated block. If the instruction is not
     // predicated, we fall through to the next case.
-    if (VF > 1 && isScalarWithPredication(I)) {
+    if (VF.isVector() && isScalarWithPredication(I)) {
       unsigned Cost = 0;
 
       // These instructions have a non-void type, so account for the phi nodes
       // that we will create. This cost is likely to be zero. The phi node
       // cost, if any, should be scaled by the block probability because it
       // models a copy at the end of each predicated block.
-      Cost += VF * TTI.getCFInstrCost(Instruction::PHI, CostKind);
+      Cost += VF.Min * TTI.getCFInstrCost(Instruction::PHI, CostKind);
 
       // The cost of the non-predicated instruction.
-      Cost += VF * TTI.getArithmeticInstrCost(I->getOpcode(), RetTy, CostKind);
+      Cost +=
+          VF.Min * TTI.getArithmeticInstrCost(I->getOpcode(), RetTy, CostKind);
 
       // The cost of insertelement and extractelement instructions needed for
       // scalarization.
       Cost += getScalarizationOverhead(I, VF);
 
       // Scale the cost by the probability of executing the predicated blocks.
       // This assumes the predicated block for each vector lane is equally
       // likely.
case Instruction::Xor: {
     Value *Op2 = I->getOperand(1);
     TargetTransformInfo::OperandValueProperties Op2VP;
     TargetTransformInfo::OperandValueKind Op2VK =
         TTI.getOperandInfo(Op2, Op2VP);
     if (Op2VK == TargetTransformInfo::OK_AnyValue && Legal->isUniform(Op2))
       Op2VK = TargetTransformInfo::OK_UniformValue;
 
     SmallVector<const Value *, 4> Operands(I->operand_values());
-    unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
+    unsigned N = isScalarAfterVectorization(I, VF) ? VF.Min : 1;
     return N * TTI.getArithmeticInstrCost(
                    I->getOpcode(), VectorTy, CostKind,
                    TargetTransformInfo::OK_AnyValue,
                    Op2VK, TargetTransformInfo::OP_None, Op2VP, Operands, I);
   }
   case Instruction::FNeg: {
-    unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
+    assert(!VF.Scalable && "VF is assumed to be non scalable.");
+    unsigned N = isScalarAfterVectorization(I, VF) ? VF.Min : 1;
     return N * TTI.getArithmeticInstrCost(
                    I->getOpcode(), VectorTy, CostKind,
                    TargetTransformInfo::OK_AnyValue,
                    TargetTransformInfo::OK_AnyValue,
                    TargetTransformInfo::OP_None, TargetTransformInfo::OP_None,
                    I->getOperand(0), I);
   }
   case Instruction::Select: {
     SelectInst *SI = cast<SelectInst>(I);
     const SCEV *CondSCEV = SE->getSCEV(SI->getCondition());
     bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop));
     Type *CondTy = SI->getCondition()->getType();
-    if (!ScalarCond)
-      CondTy = FixedVectorType::get(CondTy, VF);
-
+    if (!ScalarCond) {
+      assert(!VF.Scalable && "VF is assumed to be non scalable.");
+      CondTy = VectorType::get(CondTy, VF);
+    }
     return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy,
                                   CostKind, I);
   }
   case Instruction::ICmp:
   case Instruction::FCmp: {
     Type *ValTy = I->getOperand(0)->getType();
     Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0));
     if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF))
       ValTy = IntegerType::get(ValTy->getContext(), MinBWs[Op0AsInstruction]);
     VectorTy = ToVectorTy(ValTy, VF);
     return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, nullptr, CostKind,
                                   I);
   }
   case Instruction::Store:
   case Instruction::Load: {
-    unsigned Width = VF;
-    if (Width > 1) {
+    ElementCount Width = VF;
+    if (Width.isVector()) {
       InstWidening Decision = getWideningDecision(I, Width);
       assert(Decision != CM_Unknown &&
              "CM decision should be taken at this point");
       if (Decision == CM_Scalarize)
-        Width = 1;
+        Width = ElementCount::getFixed(1);
     }
     VectorTy = ToVectorTy(getMemInstValueType(I), Width);
     return getMemoryInstructionCost(I, VF);
   }
   case Instruction::ZExt:
   case Instruction::SExt:
   case Instruction::FPToUI:
   case Instruction::FPToSI:
   case Instruction::FPExt:
   case Instruction::PtrToInt:
   case Instruction::IntToPtr:
   case Instruction::SIToFP:
   case Instruction::UIToFP:
   case Instruction::Trunc:
   case Instruction::FPTrunc:
   case Instruction::BitCast: {
     // Computes the CastContextHint from a Load/Store instruction.
     auto ComputeCCH = [&](Instruction *I) -> TTI::CastContextHint {
       assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
              "Expected a load or a store!");
 
-      if (VF == 1 || !TheLoop->contains(I))
+      if (VF.isScalar() || !TheLoop->contains(I))
         return TTI::CastContextHint::Normal;
 
       switch (getWideningDecision(I, VF)) {
       case LoopVectorizationCostModel::CM_GatherScatter:
         return TTI::CastContextHint::GatherScatter;
       case LoopVectorizationCostModel::CM_Interleave:
         return TTI::CastContextHint::Interleave;
       case LoopVectorizationCostModel::CM_Scalarize:
if (canTruncateToMinimalBitwidth(I, VF)) {
             largestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
       } else if (Opcode == Instruction::ZExt || Opcode == Instruction::SExt) {
         SrcVecTy = largestIntegerVectorType(SrcVecTy, MinVecTy);
         VectorTy =
             smallestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
       }
     }
 
-    unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
+    assert(!VF.Scalable && "VF is assumed to be non scalable");
+    unsigned N = isScalarAfterVectorization(I, VF) ? VF.Min : 1;
     return N *
            TTI.getCastInstrCost(Opcode, VectorTy, SrcVecTy, CCH, CostKind, I);
   }
   case Instruction::Call: {
     bool NeedToScalarize;
     CallInst *CI = cast<CallInst>(I);
     unsigned CallCost = getVectorCallCost(CI, VF, NeedToScalarize);
     if (getVectorIntrinsicIDForCall(CI, TLI))
       return std::min(CallCost, getVectorIntrinsicCost(CI, VF));
     return CallCost;
   }
   default:
     // The cost of executing VF copies of the scalar instruction. This opcode
     // is unknown. Assume that it is the same as 'mul'.
-    return VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy,
-                                           CostKind) +
+    return VF.Min *
+               TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy,
            getScalarizationOverhead(I, VF);
   } // end of switch.
 }
 
 char LoopVectorize::ID = 0;
 
 static const char lv_name[] = "Loop Vectorization";
 

 static unsigned determineVPlanVF(const unsigned WidestVectorRegBits,
                                  LoopVectorizationCostModel &CM) {
   unsigned WidestType;
   std::tie(std::ignore, WidestType) = CM.getSmallestAndWidestTypes();
   return WidestVectorRegBits / WidestType;
 }
 
 VectorizationFactor
-LoopVectorizationPlanner::planInVPlanNativePath(unsigned UserVF) {
-  unsigned VF = UserVF;
+LoopVectorizationPlanner::planInVPlanNativePath(ElementCount UserVF) {
+  assert(!UserVF.Scalable && "scalable vectors not yet supported");
+  ElementCount VF = UserVF;
   // Outer loop handling: They may require CFG and instruction level
   // transformations before even evaluating whether vectorization is profitable.
   // Since we cannot modify the incoming IR, we need to build VPlan upfront in
   // the vectorization pipeline.
   if (!OrigLoop->empty()) {
     // If the user doesn't provide a vectorization factor, determine a
     // reasonable one.
-    if (!UserVF) {
-      VF = determineVPlanVF(TTI->getRegisterBitWidth(true /* Vector*/), CM);
+    if (UserVF.isZero()) {
+      VF = ElementCount::getFixed(
+          determineVPlanVF(TTI->getRegisterBitWidth(true /* Vector*/), CM));
       LLVM_DEBUG(dbgs() << "LV: VPlan computed VF " << VF << ".\n");
 
       // Make sure we have a VF > 1 for stress testing.
-      if (VPlanBuildStressTest && VF < 2) {
+      if (VPlanBuildStressTest && (VF.isScalar() || VF.isZero())) {
         LLVM_DEBUG(dbgs() << "LV: VPlan stress testing: "
                           << "overriding computed VF.\n");
-        VF = 4;
+        VF = ElementCount::getFixed(4);
       }
     }
     assert(EnableVPlanNativePath && "VPlan-native path is not enabled.");
-    assert(isPowerOf2_32(VF) && "VF needs to be a power of two");
-    LLVM_DEBUG(dbgs() << "LV: Using " << (UserVF ? "user " : "") << "VF " << VF
-                      << " to build VPlans.\n");
-    buildVPlans(VF, VF);
+    assert(isPowerOf2_32(VF.Min) && "VF needs to be a power of two");
+    LLVM_DEBUG(dbgs() << "LV: Using " << (!UserVF.isZero() ? "user " : "")
+                      << "VF " << VF << " to build VPlans.\n");
+    buildVPlans(VF.Min, VF.Min);
 
     // For VPlan build stress testing, we bail out after VPlan construction.
     if (VPlanBuildStressTest)
       return VectorizationFactor::Disabled();
 
-    return {VF, 0};
+    return {VF, 0 /*Cost*/};
   }
 
   LLVM_DEBUG(
       dbgs() << "LV: Not vectorizing. Inner loops aren't supported in the "
                 "VPlan-native path.\n");
   return VectorizationFactor::Disabled();
 }
 
-Optional<VectorizationFactor> LoopVectorizationPlanner::plan(unsigned UserVF,
-                                                             unsigned UserIC) {
+Optional<VectorizationFactor>
+LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) {
+  assert(!UserVF.Scalable && "scalable vectorization not yet handled");
   assert(OrigLoop->empty() && "Inner loop expected.");
-  Optional<unsigned> MaybeMaxVF = CM.computeMaxVF(UserVF, UserIC);
+  Optional<unsigned> MaybeMaxVF = CM.computeMaxVF(UserVF.Min, UserIC);
   if (!MaybeMaxVF) // Cases that should not to be vectorized nor interleaved.
     return None;
 
   // Invalidate interleave groups if all blocks of loop will be predicated.
   if (CM.blockNeedsPredication(OrigLoop->getHeader()) &&
       !useMaskedInterleavedAccesses(*TTI)) {
     LLVM_DEBUG(
         dbgs()
         << "LV: Invalidate all interleaved groups due to fold-tail by masking "
            "which requires masked-interleaved support.\n");
     if (CM.InterleaveInfo.invalidateGroups())
       // Invalidating interleave groups also requires invalidating all decisions
       // based on them, which includes widening decisions and uniform and scalar
       // values.
       CM.invalidateCostModelingDecisions();
   }
 
-  if (UserVF) {
+  if (!UserVF.isZero()) {
     LLVM_DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
-    assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two");
+    assert(isPowerOf2_32(UserVF.Min) && "VF needs to be a power of two");
     // Collect the instructions (and their associated costs) that will be more
     // profitable to scalarize.
     CM.selectUserVectorizationFactor(UserVF);
     CM.collectInLoopReductions();
-    buildVPlansWithVPRecipes(UserVF, UserVF);
+    buildVPlansWithVPRecipes(UserVF.Min, UserVF.Min);
     LLVM_DEBUG(printPlans(dbgs()));
     return {{UserVF, 0}};
   }
 
   unsigned MaxVF = MaybeMaxVF.getValue();
   assert(MaxVF != 0 && "MaxVF is zero.");
 
   for (unsigned VF = 1; VF <= MaxVF; VF *= 2) {
     // Collect Uniform and Scalar instructions after vectorization with VF.
-    CM.collectUniformsAndScalars(VF);
+    CM.collectUniformsAndScalars(ElementCount::getFixed(VF));
 
     // Collect the instructions (and their associated costs) that will be more
     // profitable to scalarize.
     if (VF > 1)
-      CM.collectInstsToScalarize(VF);
+      CM.collectInstsToScalarize(ElementCount::getFixed(VF));
   }
 
   CM.collectInLoopReductions();
 
   buildVPlansWithVPRecipes(1, MaxVF);
   LLVM_DEBUG(printPlans(dbgs()));
   if (MaxVF == 1)
     return VectorizationFactor::Disabled();
 
   // Select the optimal vectorization factor.
   return CM.selectVectorizationFactor(MaxVF);
 }
 
-void LoopVectorizationPlanner::setBestPlan(unsigned VF, unsigned UF) {
+void LoopVectorizationPlanner::setBestPlan(ElementCount VF, unsigned UF) {
   LLVM_DEBUG(dbgs() << "Setting best plan to VF=" << VF << ", UF=" << UF
                     << '\n');
   BestVF = VF;
   BestUF = UF;
 
   erase_if(VPlans, [VF](const VPlanPtr &Plan) {
     return !Plan->hasVF(VF);
   });
   assert(VPlans.size() == 1 && "Best VF has not a single VPlan.");
 }
 
 void LoopVectorizationPlanner::executePlan(InnerLoopVectorizer &ILV,
                                            DominatorTree *DT) {
   // Perform the actual loop transformation.
 
   // 1. Create a new empty loop. Unlink the old loop and connect the new one.
   VPCallbackILV CallbackILV(ILV);
 
-  VPTransformState State{BestVF, BestUF,      LI,
-                         DT,     ILV.Builder, ILV.VectorLoopValueMap,
-                         &ILV,   CallbackILV};
+  assert(BestVF.hasValue() && "Vectorization Factor is missing");
+
+  VPTransformState State{*BestVF, BestUF,      LI,
+                         DT,      ILV.Builder, ILV.VectorLoopValueMap,
+                         &ILV,    CallbackILV};
   State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton();
   State.TripCount = ILV.getOrCreateTripCount(nullptr);
   State.CanonicalIV = ILV.Induction;
 
   //===------------------------------------------------===//
   //
   // Notice: any optimization or new instruction that go
   // into the code below should also be implemented in
if (!IsUnrollMetadata) {
     MDNode *NewLoopID = MDNode::get(Context, MDs);
     // Set operand 0 to refer to the loop id itself.
     NewLoopID->replaceOperandWith(0, NewLoopID);
     L->setLoopID(NewLoopID);
   }
 }
 
 bool LoopVectorizationPlanner::getDecisionAndClampRange(
-    const std::function<bool(unsigned)> &Predicate, VFRange &Range) {
+    const std::function<bool(ElementCount)> &Predicate, VFRange &Range) {
   assert(Range.End > Range.Start && "Trying to test an empty VF range.");
-  bool PredicateAtRangeStart = Predicate(Range.Start);
+  bool PredicateAtRangeStart = Predicate(ElementCount::getFixed(Range.Start));
 
   for (unsigned TmpVF = Range.Start * 2; TmpVF < Range.End; TmpVF *= 2)
-    if (Predicate(TmpVF) != PredicateAtRangeStart) {
+    if (Predicate(ElementCount::getFixed(TmpVF)) != PredicateAtRangeStart) {
       Range.End = TmpVF;
       break;
     }
 
   return PredicateAtRangeStart;
 }
 
 /// Build VPlans for the full range of feasible VF's = {\p MinVF, 2 * \p MinVF,

 }
 
 VPWidenMemoryInstructionRecipe *
 VPRecipeBuilder::tryToWidenMemory(Instruction *I, VFRange &Range,
                                   VPlanPtr &Plan) {
   assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
          "Must be called with either a load or store");
 
-  auto willWiden = [&](unsigned VF) -> bool {
-    if (VF == 1)
+  auto willWiden = [&](ElementCount VF) -> bool {
+    assert(!VF.Scalable && "unexpected scalable ElementCount");
+    if (VF.isScalar())
       return false;
     LoopVectorizationCostModel::InstWidening Decision =
         CM.getWideningDecision(I, VF);
     assert(Decision != LoopVectorizationCostModel::CM_Unknown &&
            "CM decision should be taken at this point.");
     if (Decision == LoopVectorizationCostModel::CM_Interleave)
       return true;
     if (CM.isScalarAfterVectorization(I, VF) ||
VPRecipeBuilder::tryToOptimizeInductionTruncate(TruncInst *I,
   // Optimize the special case where the source is a constant integer
   // induction variable. Notice that we can only optimize the 'trunc' case
   // because (a) FP conversions lose precision, (b) sext/zext may wrap, and
   // (c) other casts depend on pointer size.
 
   // Determine whether \p K is a truncation based on an induction variable that
   // can be optimized.
   auto isOptimizableIVTruncate =
-      [&](Instruction *K) -> std::function<bool(unsigned)> {
-    return
-        [=](unsigned VF) -> bool { return CM.isOptimizableIVTruncate(K, VF); };
+      [&](Instruction *K) -> std::function<bool(ElementCount)> {
+    return [=](ElementCount VF) -> bool {
+      return CM.isOptimizableIVTruncate(K, VF);
+    };
   };
 
   if (LoopVectorizationPlanner::getDecisionAndClampRange(
           isOptimizableIVTruncate(I), Range))
     return new VPWidenIntOrFpInductionRecipe(cast<PHINode>(I->getOperand(0)),
                                              I);
   return nullptr;
 }
VPBlendRecipe *VPRecipeBuilder::tryToBlend(PHINode *Phi, VPlanPtr &Plan) {
   }
   return new VPBlendRecipe(Phi, Operands);
 }
 
 VPWidenCallRecipe *VPRecipeBuilder::tryToWidenCall(CallInst *CI, VFRange &Range,
                                                    VPlan &Plan) const {
 
   bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange(
-      [this, CI](unsigned VF) { return CM.isScalarWithPredication(CI, VF); },
+      [this, CI](ElementCount VF) {
+        return CM.isScalarWithPredication(CI, VF);
+      },
       Range);
 
   if (IsPredicated)
     return nullptr;
 
   Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
   if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end ||
              ID == Intrinsic::lifetime_start || ID == Intrinsic::sideeffect))
     return nullptr;
 
-  auto willWiden = [&](unsigned VF) -> bool {
+  auto willWiden = [&](ElementCount VF) -> bool {
     Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
     // The following case may be scalarized depending on the VF.
     // The flag shows whether we use Intrinsic or a usual Call for vectorized
     // version of the instruction.
     // Is it beneficial to perform intrinsic call compared to lib call?
     bool NeedToScalarize = false;
     unsigned CallCost = CM.getVectorCallCost(CI, VF, NeedToScalarize);
     bool UseVectorIntrinsic =
         ID && CM.getVectorIntrinsicCost(CI, VF) <= CallCost;
     return UseVectorIntrinsic || !NeedToScalarize;
   };
 
   if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range))
     return nullptr;
 
   return new VPWidenCallRecipe(*CI, Plan.mapToVPValues(CI->arg_operands()));
 }
 
 bool VPRecipeBuilder::shouldWiden(Instruction *I, VFRange &Range) const {
   assert(!isa<BranchInst>(I) && !isa<PHINode>(I) && !isa<LoadInst>(I) &&
          !isa<StoreInst>(I) && "Instruction should have been handled earlier");
   // Instruction should be widened, unless it is scalar after vectorization,
   // scalarization is profitable or it is predicated.
-  auto WillScalarize = [this, I](unsigned VF) -> bool {
+  auto WillScalarize = [this, I](ElementCount VF) -> bool {
     return CM.isScalarAfterVectorization(I, VF) ||
            CM.isProfitableToScalarize(I, VF) ||
            CM.isScalarWithPredication(I, VF);
   };
   return !LoopVectorizationPlanner::getDecisionAndClampRange(WillScalarize,
                                                              Range);
 }
 
VPWidenRecipe *VPRecipeBuilder::tryToWiden(Instruction *I, VPlan &Plan) const {
   return new VPWidenRecipe(*I, Plan.mapToVPValues(I->operands()));
 }
 
 VPBasicBlock *VPRecipeBuilder::handleReplication(
     Instruction *I, VFRange &Range, VPBasicBlock *VPBB,
     DenseMap<Instruction *, VPReplicateRecipe *> &PredInst2Recipe,
     VPlanPtr &Plan) {
   bool IsUniform = LoopVectorizationPlanner::getDecisionAndClampRange(
-      [&](unsigned VF) { return CM.isUniformAfterVectorization(I, VF); },
+      [&](ElementCount VF) { return CM.isUniformAfterVectorization(I, VF); },
       Range);
 
   bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange(
-      [&](unsigned VF) { return CM.isScalarWithPredication(I, VF); }, Range);
+      [&](ElementCount VF) { return CM.isScalarWithPredication(I, VF); },
+      Range);
 
   auto *Recipe = new VPReplicateRecipe(I, Plan->mapToVPValues(I->operands()),
                                        IsUniform, IsPredicated);
   setRecipe(I, Recipe);
 
   // Find if I uses a predicated instruction. If so, it will use its scalar
   // value. Avoid hoisting the insert-element which packs the scalar value into
   // a vector value, as that happens iff all users use the vector value.
for (auto &Reduction : CM.getInLoopReductionChains()) {
     }
   }
 
   // For each interleave group which is relevant for this (possibly trimmed)
   // Range, add it to the set of groups to be later applied to the VPlan and add
   // placeholders for its members' Recipes which we'll be replacing with a
   // single VPInterleaveRecipe.
   for (InterleaveGroup<Instruction> *IG : IAI.getInterleaveGroups()) {
-    auto applyIG = [IG, this](unsigned VF) -> bool {
-      return (VF >= 2 && // Query is illegal for VF == 1
+    auto applyIG = [IG, this](ElementCount VF) -> bool {
+      return (VF.isVector() && // Query is illegal for VF == 1
               CM.getWideningDecision(IG->getInsertPos(), VF) ==
                   LoopVectorizationCostModel::CM_Interleave);
     };
     if (!getDecisionAndClampRange(applyIG, Range))
       continue;
     InterleaveGroups.insert(IG);
     for (unsigned i = 0; i < IG->getFactor(); i++)
       if (Instruction *Member = IG->getMember(i))
for (auto &Reduction : Legal->getReductionVars()) {
       VPValue *Phi = Plan->getVPValue(Reduction.first);
       VPValue *Red = Plan->getVPValue(Reduction.second.getLoopExitInstr());
       Builder.createNaryOp(Instruction::Select, {Cond, Red, Phi});
     }
   }
 
   std::string PlanName;
   raw_string_ostream RSO(PlanName);
-  unsigned VF = Range.Start;
+  ElementCount VF = ElementCount::getFixed(Range.Start);
   Plan->addVF(VF);
   RSO << "Initial VPlan for VF={" << VF;
-  for (VF *= 2; VF < Range.End; VF *= 2) {
+  for (VF.Min *= 2; VF.Min < Range.End; VF.Min *= 2) {
     Plan->addVF(VF);
     RSO << "," << VF;
   }
   RSO << "},UF>=1";
   RSO.flush();
   Plan->setName(PlanName);
 
   return Plan;
VPlanPtr LoopVectorizationPlanner::buildVPlan(VFRange &Range) {
   // Create new empty VPlan
   auto Plan = std::make_unique<VPlan>();
 
   // Build hierarchical CFG
   VPlanHCFGBuilder HCFGBuilder(OrigLoop, LI, *Plan);
   HCFGBuilder.buildHierarchicalCFG();
 
   for (unsigned VF = Range.Start; VF < Range.End; VF *= 2)
-    Plan->addVF(VF);
+    Plan->addVF(ElementCount::getFixed(VF));
 
   if (EnableVPlanPredication) {
     VPlanPredicator VPP(*Plan);
     VPP.predicate();
 
     // Avoid running transformation to recipes until masked code generation in
     // VPlan-native path is in place.
     return Plan;
void VPReductionRecipe::execute(VPTransformState &State) {
   }
 }
 
 void VPReplicateRecipe::execute(VPTransformState &State) {
   if (State.Instance) { // Generate a single instance.
     State.ILV->scalarizeInstruction(Ingredient, User, *State.Instance,
                                     IsPredicated, State);
     // Insert scalar instance packing it into a vector.
-    if (AlsoPack && State.VF > 1) {
+    if (AlsoPack && State.VF.isVector()) {
       // If we're constructing lane 0, initialize to start from undef.
       if (State.Instance->Lane == 0) {
-        Value *Undef = UndefValue::get(
-            FixedVectorType::get(Ingredient->getType(), State.VF));
+        assert(!State.VF.Scalable && "VF is assumed to be non scalable.");
+        Value *Undef =
+            UndefValue::get(VectorType::get(Ingredient->getType(), State.VF));
         State.ValueMap.setVectorValue(Ingredient, State.Instance->Part, Undef);
       }
       State.ILV->packScalarIntoVectorValue(Ingredient, *State.Instance);
     }
     return;
   }
 
   // Generate scalar instances for all VF lanes of all UF parts, unless the
   // instruction is uniform inwhich case generate only the first lane for each
   // of the UF parts.
-  unsigned EndLane = IsUniform ? 1 : State.VF;
+  unsigned EndLane = IsUniform ? 1 : State.VF.Min;
   for (unsigned Part = 0; Part < State.UF; ++Part)
     for (unsigned Lane = 0; Lane < EndLane; ++Lane)
       State.ILV->scalarizeInstruction(Ingredient, User, {Part, Lane},
                                       IsPredicated, State);
 }
 
 void VPBranchOnMaskRecipe::execute(VPTransformState &State) {
   assert(State.Instance && "Branch on Mask works only on single instance.");
static bool processLoopInVPlanNativePath(
   // TODO: CM is not used at this point inside the planner. Turn CM into an
   // optional argument if we don't need it in the future.
   LoopVectorizationPlanner LVP(L, LI, TLI, TTI, LVL, CM, IAI, PSE);
 
   // Get user vectorization factor.
   const unsigned UserVF = Hints.getWidth();
 
   // Plan how to best vectorize, return the best VF and its cost.
-  const VectorizationFactor VF = LVP.planInVPlanNativePath(UserVF);
+  const VectorizationFactor VF =
+      LVP.planInVPlanNativePath(ElementCount::getFixed(UserVF));
 
   // If we are stress testing VPlan builds, do not attempt to generate vector
   // code. Masked vector code generation support will follow soon.
   // Also, do not attempt to vectorize if no vector code will be produced.
   if (VPlanBuildStressTest || EnableVPlanPredication ||
       VectorizationFactor::Disabled() == VF)
     return false;
 
#endif /* NDEBUG */
   // Use the planner for vectorization.
   LoopVectorizationPlanner LVP(L, LI, TLI, TTI, &LVL, CM, IAI, PSE);
 
   // Get user vectorization factor and interleave count.
   unsigned UserVF = Hints.getWidth();
   unsigned UserIC = Hints.getInterleave();
 
   // Plan how to best vectorize, return the best VF and its cost.
-  Optional<VectorizationFactor> MaybeVF = LVP.plan(UserVF, UserIC);
+  Optional<VectorizationFactor> MaybeVF =
+      LVP.plan(ElementCount::getFixed(UserVF), UserIC);
 
   VectorizationFactor VF = VectorizationFactor::Disabled();
   unsigned IC = 1;
 
   if (MaybeVF) {
     VF = *MaybeVF;
     // Select the interleave count.
     IC = CM.selectInterleaveCount(VF.Width, VF.Cost);

llvm/lib/Transforms/Vectorize/VPlan.h

struct VectorizerValueMap {
   friend struct VPTransformState;
 
 private:
   /// The unroll factor. Each entry in the vector map contains UF vector values.
   unsigned UF;
 
   /// The vectorization factor. Each entry in the scalar map contains UF x VF
   /// scalar values.
-  unsigned VF;
+  ElementCount VF;
 
   /// The vector and scalar map storage. We use std::map and not DenseMap
   /// because insertions to DenseMap invalidate its iterators.
   using VectorParts = SmallVector<Value *, 2>;
   using ScalarParts = SmallVector<SmallVector<Value *, 4>, 2>;
   std::map<Value *, VectorParts> VectorMapStorage;
   std::map<Value *, ScalarParts> ScalarMapStorage;
 
 public:
   /// Construct an empty map with the given unroll and vectorization factors.
-  VectorizerValueMap(unsigned UF, unsigned VF) : UF(UF), VF(VF) {}
+  VectorizerValueMap(unsigned UF, ElementCount VF) : UF(UF), VF(VF) {}
 
   /// \return True if the map has any vector entry for \p Key.
   bool hasAnyVectorValue(Value *Key) const {
     return VectorMapStorage.count(Key);
   }
 
   /// \return True if the map has a vector entry for \p Key and \p Part.
   bool hasVectorValue(Value *Key, unsigned Part) const {
     assert(Part < UF && "Queried Vector Part is too large.");
     if (!hasAnyVectorValue(Key))
       return false;
     const VectorParts &Entry = VectorMapStorage.find(Key)->second;
     assert(Entry.size() == UF && "VectorParts has wrong dimensions.");
     return Entry[Part] != nullptr;
   }
 
   /// \return True if the map has any scalar entry for \p Key.
   bool hasAnyScalarValue(Value *Key) const {
     return ScalarMapStorage.count(Key);
   }
 
   /// \return True if the map has a scalar entry for \p Key and \p Instance.
   bool hasScalarValue(Value *Key, const VPIteration &Instance) const {
     assert(Instance.Part < UF && "Queried Scalar Part is too large.");
-    assert(Instance.Lane < VF && "Queried Scalar Lane is too large.");
+    assert(Instance.Lane < VF.Min && "Queried Scalar Lane is too large.");
+    assert(!VF.Scalable && "VF is assumed to be non scalable.");
+
     if (!hasAnyScalarValue(Key))
       return false;
     const ScalarParts &Entry = ScalarMapStorage.find(Key)->second;
     assert(Entry.size() == UF && "ScalarParts has wrong dimensions.");
-    assert(Entry[Instance.Part].size() == VF &&
+    assert(Entry[Instance.Part].size() == VF.Min &&
            "ScalarParts has wrong dimensions.");
     return Entry[Instance.Part][Instance.Lane] != nullptr;
   }
 
   /// Retrieve the existing vector value that corresponds to \p Key and
   /// \p Part.
   Value *getVectorValue(Value *Key, unsigned Part) {
     assert(hasVectorValue(Key, Part) && "Getting non-existent value.");
public:
   /// value is not already set.
   void setScalarValue(Value *Key, const VPIteration &Instance, Value *Scalar) {
     assert(!hasScalarValue(Key, Instance) && "Scalar value already set");
     if (!ScalarMapStorage.count(Key)) {
       ScalarParts Entry(UF);
       // TODO: Consider storing uniform values only per-part, as they occupy
       //       lane 0 only, keeping the other VF-1 redundant entries null.
       for (unsigned Part = 0; Part < UF; ++Part)
-        Entry[Part].resize(VF, nullptr);
+        Entry[Part].resize(VF.Min, nullptr);
       ScalarMapStorage[Key] = Entry;
     }
     ScalarMapStorage[Key][Instance.Part][Instance.Lane] = Scalar;
   }
 
   /// Reset the vector value associated with \p Key for the given \p Part.
   /// This function can be used to update values that have already been
   /// vectorized. This is the case for "fix-up" operations including type
struct VPCallback {
   virtual Value *getOrCreateVectorValues(Value *V, unsigned Part) = 0;
   virtual Value *getOrCreateScalarValue(Value *V,
                                         const VPIteration &Instance) = 0;
 };
 
 /// VPTransformState holds information passed down when "executing" a VPlan,
 /// needed for generating the output IR.
 struct VPTransformState {
-  VPTransformState(unsigned VF, unsigned UF, LoopInfo *LI, DominatorTree *DT,
-                   IRBuilder<> &Builder, VectorizerValueMap &ValueMap,
-                   InnerLoopVectorizer *ILV, VPCallback &Callback)
+  VPTransformState(ElementCount VF, unsigned UF, LoopInfo *LI,
+                   DominatorTree *DT, IRBuilder<> &Builder,
+                   VectorizerValueMap &ValueMap, InnerLoopVectorizer *ILV,
+                   VPCallback &Callback)
       : VF(VF), UF(UF), Instance(), LI(LI), DT(DT), Builder(Builder),
         ValueMap(ValueMap), ILV(ILV), Callback(Callback) {}
 
   /// The chosen Vectorization and Unroll Factors of the loop being vectorized.
-  unsigned VF;
+  ElementCount VF;
   unsigned UF;
 
   /// Hold the indices to generate specific scalar instructions. Null indicates
   /// that all instances are to be generated, using either scalar or vector
   /// instructions.
   Optional<VPIteration> Instance;
 
   struct DataState {

 class VPlan {
   friend class VPlanPrinter;
   friend class VPSlotTracker;
 
   /// Hold the single entry to the Hierarchical CFG of the VPlan.
   VPBlockBase *Entry;
 
   /// Holds the VFs applicable to this VPlan.
-  SmallSet<unsigned, 2> VFs;
+  SmallSetVector<ElementCount, 2> VFs;
 
   /// Holds the name of the VPlan, for printing.
   std::string Name;
 
   /// Holds all the external definitions created for this VPlan.
   // TODO: Introduce a specific representation for external definitions in
   // VPlan. External definitions must be immutable and hold a pointer to its
   // underlying IR that will be used to implement its structural comparison
public:
 
   /// The backedge taken count of the original loop.
   VPValue *getOrCreateBackedgeTakenCount() {
     if (!BackedgeTakenCount)
       BackedgeTakenCount = new VPValue();
     return BackedgeTakenCount;
   }
 
-  void addVF(unsigned VF) { VFs.insert(VF); }
+  void addVF(ElementCount VF) { VFs.insert(VF); }
 
-  bool hasVF(unsigned VF) { return VFs.count(VF); }
+  bool hasVF(ElementCount VF) { return VFs.count(VF); }
 
   const std::string &getName() const { return Name; }
 
   void setName(const Twine &newName) { Name = newName.str(); }
 
   /// Add \p VPVal to the pool of external definitions if it's not already
   /// in the pool.
   void addExternalDef(VPValue *VPVal) {

llvm/lib/Transforms/Vectorize/VPlan.cpp

void VPRegionBlock::execute(VPTransformState *State) {
 
   assert(!State->Instance && "Replicating a Region with non-null instance.");
 
   // Enter replicating mode.
   State->Instance = {0, 0};
 
   for (unsigned Part = 0, UF = State->UF; Part < UF; ++Part) {
     State->Instance->Part = Part;
-    for (unsigned Lane = 0, VF = State->VF; Lane < VF; ++Lane) {
+    assert(!State->VF.Scalable && "VF is assumed to be non scalable.");
+    for (unsigned Lane = 0, VF = State->VF.Min; Lane < VF; ++Lane) {
       State->Instance->Lane = Lane;
       // Visit the VPBlocks connected to \p this, starting from it.
       for (VPBlockBase *Block : RPOT) {
         LLVM_DEBUG(dbgs() << "LV: VPBlock in RPO " << Block->getName() << '\n');
         Block->execute(State);
       }
     }
   }
void VPInstruction::generateInstruction(VPTransformState &State,
   }
   case VPInstruction::ActiveLaneMask: {
     // Get first lane of vector induction variable.
     Value *VIVElem0 = State.get(getOperand(0), {Part, 0});
     // Get first lane of backedge-taken-count.
     Value *ScalarBTC = State.get(getOperand(1), {Part, 0});
 
     auto *Int1Ty = Type::getInt1Ty(Builder.getContext());
-    auto *PredTy = FixedVectorType::get(Int1Ty, State.VF);
+    auto *PredTy = FixedVectorType::get(Int1Ty, State.VF.Min);
     Instruction *Call = Builder.CreateIntrinsic(
         Intrinsic::get_active_lane_mask, {PredTy, ScalarBTC->getType()},
         {VIVElem0, ScalarBTC}, nullptr, "active.lane.mask");
     State.set(this, Call, Part);
     break;
   }
   default:
     llvm_unreachable("Unsupported opcode for instruction");
if (Mask) {
     Mask->printAsOperand(O, SlotTracker);
   }
 }
 
 void VPWidenCanonicalIVRecipe::execute(VPTransformState &State) {
   Value *CanonicalIV = State.CanonicalIV;
   Type *STy = CanonicalIV->getType();
   IRBuilder<> Builder(State.CFG.PrevBB->getTerminator());
-  auto VF = State.VF;
-  Value *VStart = VF == 1
-                      ? CanonicalIV
-                      : Builder.CreateVectorSplat(VF, CanonicalIV, "broadcast");
+  ElementCount VF = State.VF;
+  assert(!VF.Scalable && "the code following assumes non scalables ECs");
+  Value *VStart = VF.isScalar() ? CanonicalIV
+                                : Builder.CreateVectorSplat(VF.Min, CanonicalIV,
+                                                            "broadcast");
   for (unsigned Part = 0, UF = State.UF; Part < UF; ++Part) {
     SmallVector<Constant *, 8> Indices;
-    for (unsigned Lane = 0; Lane < VF; ++Lane)
-      Indices.push_back(ConstantInt::get(STy, Part * VF + Lane));
+    for (unsigned Lane = 0; Lane < VF.Min; ++Lane)
+      Indices.push_back(ConstantInt::get(STy, Part * VF.Min + Lane));
     // If VF == 1, there is only one iteration in the loop above, thus the
     // element pushed back into Indices is ConstantInt::get(STy, Part)
     Constant *VStep = VF == 1 ? Indices.back() : ConstantVector::get(Indices);
     // Add the consecutive indices to the vector value.
     Value *CanonicalVectorIV = Builder.CreateAdd(VStart, VStep, "vec.iv");
     State.set(getVPValue(), CanonicalVectorIV, Part);
   }
 }