[cfe-dev] RFC: Interface user provided vector functions with the vectorizer.

Tue Jun 11 13:55:18 PDT 2019

Dear all,

I have re-written the proposal for interfacing user provided vector
functions, originally posted in both llvm-dev and cfe-dev mailing
list:

"[RFC] Expose user provided vector function for auto-vectorization."

The proposal looks quite different from the original submission,
therefore I took the liberty to start a new thread.

The original thread generated some good discussion. In particular,
Simon Moll and Johannes Doerfert (CCed) have managed to provide good
arguments for the following claims:

1. The Vector Function ABI name mangling scheme of a target is not
   enough to describe all uses cases of function vectorization that
   the compiler might end up needing to support in the future.
2. `declare variant` needs to be handled properly at IR level, to be
   able to give the compiler the full OpenMP context of the directive.

This proposal addresses those two concerns and other (I believe) minor
concerns that have been raised in the previous thread.

This proposal is provided with examples and a self assessment around
extendibility.

I have CCed all the people that have participated in the discussion so
far, please let me know if you think I have missed anything of what
have been raised.

Kind regards,

Francesco

*** DRAFT OF THE PROPOSAL ***

# SCOPE OF THE RFC : Interface user provided vector functions with the vectorizer.

Because the users care about portability (across compilers, libraries
and systems), I believe we have to base sour solution on a standard
that describes the mapping from the scalar function to the vector
function.

Because OpenMP is standard and widely used, we should base our
solution on the mechanisms that the standard provides, via the
directives `declare simd` and `declare variant`, the latter when used
in with the `simd` trait in the `construct` set.

Please notice that:

1. The scope of the proposal is not implementing full support for
   `pragma omp declare variant`.
2. The scope of the proposal is not enabling the vectorizer to do new
   kind of vectorizations (e.g. RV-like vectorization described by
   Simon).
3. The proposal aims to be extendible wrt 1. and 2.
4. The IR attribute introduced in this proposal is equivalent to the
   one needed for the VecClone pass under development in
   https://reviews.llvm.org/D22792

# CLANG COMPONENTS

A C function attribute, `clang_declare_simd_variant`, to attach to the
scalar version. The attribute provides enough information to the
compiler about the vector shape of the user defined function. The
vector shapes handled by the attribute are those handled by the OpenMP
standard via `declare simd` (and no more than that).

1. The function attribute handling in clang is crafted with the
   requirement that it will be possible to re-use the same components
   for the info generated by `declare variant` when used with a `simd`
   traits in the `construct` set.
2. The attribute allows orthogonality with the vectorization that is
   done via OpenMP: the user vector function is still exposed for
   vectorization when not using `-fopenmp-[simd]` once the `declare
   simd` and `declare variant` directive of OpenMP will be available
   in the front-end.

## C function attribute: `clang_declare_simd_variant`

The definition of this attribute has been crafted to match the
semantics of `declare variant` for a `simd` construct described in
OpenMP 5.0. I have added only the traits of the `device` set, `isa`
and `arch`, which I believe are enough to cover for the use case of
this proposal. If that is not the case, please provide an example,
extending the attribute will be easy even once the current one is
implemented.

```
clang_declare_simd_variant(<variant-func-id>, <simd clauses>{, <context selector clauses>})

<variant-func-id>:= The name of a function variant that is a base language identifier, or,
                    for C++, a template-id.

<simd clauses> := <simdlen>, <mask>{, <optional simd clauses>}

<simdlen> := simdlen(<positive number>) | simdlen("scalable")

<mask>    := inbranch | notinbranch

<optional simd clauses> := <linear clause> 
                         | <uniform clause>
                         | <align clause>  | {,<optional simd clauses>}

<linear clause>  := linear_ref(<var>,<step>)
                  | linear_var(<var>, <step>)
                  | linear_uval(<var>, <step>)
                  | linear(<var>, <step>)

<step> := <var> | <non zero number>

<uniform clause> := uniform(<var>)

<align clause>   := align(<var>, <positive number>)

<var> := Name of a parameter in the scalar function declaration/definition

<non zero number> := ... | -2 | -1 | 1 | 2 | ...

<positive number> := 1 | 2 | 3 | ...

<context selector clauses> := {<isa>}{,} {<arch>}

<isa> := isa(target-specific-value)

<arch> := arch(target-specific-value)

```

# LLVM COMPONENTS:

## VectorFunctionShape class 

The object `VectorFunctionShape` contains the information about the
kind of vectorization available for an `llvm::Call`.

The object `VectorFunctionShape` must contain the following information:

1. Vectorization Factor (or number or concurrent lanes executed by the
   SIMD version of the function). Encoded by unsigned integer.
2. Whether the vector function is requested for scalable
   vectorization, encoded by a boolean.
3. Information about masking / no masking, encoded by a boolean.
4. Information about the parameters, encoded in a container that
   carries objects of type `ParamaterType`, to describe features like
   `linear` and `uniform`.
5. Function name redirection, if a user has specified to use a custom
   name instead of the Vector Function ABI ones.

Items 1. to 5. represents the information stored in the
`vector-function-abi-variant` attribute (see next section).

The object can be extended in the future to include new vectorization
kinds (for example the RV-like vectorization of the Region
Vectorizer), or to add more context information that might come from
other uses of OpenMP `declare variant`, or to add new Vector Function
ABIs not based on OpenMP. Such information can be retrieved by
attributes that will be added to describe the `Call` instance.

## IR Attribute

We define a `vector-function-abi-variant` attribute that lists the
mangled names produced via the mangling function of the Vector
Function ABI rules.

```
vector-function-abi-variant = "abi_mangled_name_01, abi_mangled_name_02(user_redirection),..."
```

1. Because we use only OpenMP `declare simd` vectorization, and
   because we require a vector Function ABI, we make this explicit
   in the name of the attribute.
2. Because the Vector Function ABIs encode all the information
   needed to know the vectorization shape of the vector function in
   the mangled names, we provide the mangled name via the
   attribute.
3. Function names redirection is specified by enclosing the name of
   the redirection in parenthesis, as in
   `abi_mangled_name_02(user_redirection)`.

## Vector ABI Demangler

The “Vector ABI demangler”, is the component that demangles the data
in the `vector-function-abi-variant` attribute and that provides the
instances of the class `VectorFunctionShape` that can be derived by
the mangled names listed in the attribute.

## Query interface: Search Vector Function System (SVFS)

An interface that can be queried by the LLVM components to understand
whether or not a scalar function can be vectorized, and that retrieves
the vector function to be used if such vector shape is available.

1. This component is going to be unrelated to OpenMP.
2. This component will use internally the demangler defined in the
   previous section, but it will not expose any aspect of the Vector
   Function ABI via its interface.

The interface provides two methods.

```
std::vector<VectorFunctionShape> SVFS::isFunctionVectorizable(llvm::CallInst * Call);

llvm::Function * SVFS::getVectorizedFunction(llvm::CallInst * Call, VectorFunctionShape Info);
```

The first method is used to list all the vector shapes that available
and attached to a scalar function. An empty results means that no
vector versions are available.

The second method retrieves the information needed to build a call to
a vector function with a specific `VectorFunctionShape` info.

# (SELF) ASSESSMENT ON EXTENDIBILITY

1. Extending the C function attribute `clang_declare_simd_variant` to
   new Vector Function ABIs that use OpenMP will be straightforward
   because the attribute is tight to such ABIs and OpenMP.
2. The C attribute `clang_declare_simd_variant` and the `declare
   variant` directive used for the `simd` trait will be sharing the
   internals in clang, so adding the OpenMP functionality for `simd`
   traits will be mostly handling the directive in the OpenMP
   parser. How this should be done is described in
   https://clang.llvm.org/docs/InternalsManual.html#how-to-add-an-attribute
3. The IR attribute `vector-function-abi-variant` is not to be
   extended to represent other kind of vectorization other than those
   handled by `declare simd` and that are handled with a Vector
   Function ABI.
4. The IR attribute `vector-function-abi-variant` is not defined to be
   extended to represent the information of `declare variant` in its
   totality.
5. The IR attribute will not need to change when we will introduce non
   vector function ABI vectorization (RV-like, reductions...) or when
   we will decide to fully support `declare variant`. The information
   it carries will not need to be invalidated, but just extended with
   new attributes that will need to be handled by the
   `VectorFunctionShape` class, in a similar way the
   `llvm::FPMathOperator` does with the `llvm::FastMathFlags`, which
   operates on individual attributes to describe an overall
   functionality.

# Examples

## Example 1 

Exposing an Advanced SIMD vector function when targeting Advanced SIMD
in AArch64.

```
double foo_01(double Input) __attribute__(clang_declare_simd_variant(“vector_foo_01", simdlen(2), notinbranch, isa("simd"));

// Advanced SIMD version
float64x2_t vector_foo_01(float64x2_t VectorInput);
```

The resulting IR attribute is:

```
attribute #0 = {vector-abi-variant="_ZGVnN2v_foo_01(vector_foo_01)"}
```

## Example 2

Exposing an Advanced SIMD vector function when targeting Advanced SIMD
in AArch64, but with the wrong signature. The user specifies a masked
version of the function in the clauses of the attribute, the compiler
throws an error suggesting the signature expected for
``vector_foo_02.``

```
double foo_02(double Input) __attribute__(clang_declare_simd_variant(“vector_foo_02", simdlen(2), inbranch, isa("simd"));

// Advanced SIMD version
float64x2_t vector_foo_02(float64x2_t VectorInput); 
// (suggested) compiler error ->                      ^ Missing mask parameter of type `uint64x2_t`.
``` 

## Example 3 

Targeting `sincos`-like signatures.

```
void foo_03(double Input, double * Output) __attribute__(clang_declare_simd_variant(“vector_foo_03", simdlen(2), notinbranch, linear(Output, 1), isa("simd"));

// Advanced SIMD version
void vector_foo_03(float64x2_t VectorInput, double * Output); 
``` 

The resulting IR attribute is:

```
attribute #0 = {vector-abi-variant="_ZGVnN2vl8_foo_03(vector_foo_03)"}
```
## Example 4

Scalable vectorization targeting SVE

```
double foo_04(double Input) __attribute__(clang_declare_simd_variant(“vector_foo_04", simdlen("scalable"), notinbranch, isa("sve"));

// SVE version
svfloat64_t vector_foo_04(svfloat64_t VectorInput, svbool_t Mask);
```

The resulting IR attribute is:

```
attribute #0 = {vector-abi-variant="_ZGVsM2v_foo_04(vector_foo_04)"}
```

## Example 5

Fixed length vectorization targeting SVE

```
double foo_05(double Input) __attribute__(clang_declare_simd_variant(“vector_foo_05", simdlen(4), inbranch, isa("sve"));

// Fixed-length SVE version
svfloat64_t vector_foo_05(svfloat64_t VectorInput, svbool_t Mask);
```

The resulting IR attribute is:

```
attribute #0 = {vector-abi-variant="_ZGVsM2v_foo_04(vector_foo_04)"}
```

## Example 06

This is an x86 example, equivalent to the one provided by Andrei
Elovikow in
http://lists.llvm.org/pipermail/llvm-dev/2019-June/132885.html. Godbolt
rendering with ICC at https://godbolt.org/z/Of1NxZ

```
float MyAdd(float* a, int b) __attribute__(clang_declare_simd_variant(“MyAddVec", simdlen(8), notinbranch, arch("core_2nd_gen_avx"))
{ 
  return *a + b;
}

__m256 MyAddVec(float* v_a, __m128i v_b1, __m128i v_b2);
```

The resulting IR attribute is:

```
attribute #0 = {vector-abi-variant="_ZGVbN8l4v_MyAdd(MyAddVec)"}
```

## Example showing interaction with `declare simd`

```
#pragma omp declare simd linear(a) notinbranch
float foo_06(float *a, int x) __attribute__(clang_declare_simd_variant(“vector_foo_06", simdlen(4), linear(a), notinbranch, arch("armv8.2-a+simd")) {
    return *a + x;
}

// Advanced SIMD version
float32x4_t vector_foo_06(float *a, int32x4_t vx) {
// Custom implementation.
}
```

The resulting IR attribute is made of three symbols:

1. `_ZGVnN2l4v_foo_06` and `_ZGVnN4l4v_foo_06`, which represent the
   ones the compiler builds by auto-vectorizing `foo_06` according to
   the rule defined in the Vector Function ABI specifications for
   AArch64.
2. `_ZGVnN4l4v_foo_06(vector_foo_06)`, which represents the
   user-defined redirection of the 4-lane version of `foo_06` to the
   custom implementation provided by the user when targeting Advanced
   SIMD for version 8.2 of the A64 instruction set.

```
attribute #0 = {vector-function-abi-variant="_ZGVnN2l4v_foo_06,_ZGVnN4l4v_foo_06,_ZGVnN4l4v_foo_06(vector_foo_06)"}
```