llvm-project/mlir/docs/Dialects/Vector.md

# 'vector' Dialect

[TOC]

MLIR supports multi-dimensional `vector` types and custom operations on those
types. A generic, retargetable, higher-order ``vector`` type (`n-D` with `n >
1`) is a structured type, that carries semantic information useful for
transformations. This document discusses retargetable abstractions that exist
in MLIR today and operate on ssa-values of type `vector` along with pattern
rewrites and lowerings that enable targeting specific instructions on concrete
targets. These abstractions serve to separate concerns between operations on
`memref` (a.k.a buffers) and operations on ``vector`` values. This is not a
new proposal but rather a textual documentation of existing MLIR components
along with a rationale.

## Positioning in the Codegen Infrastructure
The following diagram, recently presented with the [StructuredOps
abstractions](https://drive.google.com/corp/drive/u/0/folders/1sRAsgsd8Bvpm_IxREmZf2agsGU2KvrK-),
captures the current codegen paths implemented in MLIR in the various existing
lowering paths.
![](https://user-images.githubusercontent.com/10148468/71177417-f78e4d80-2239-11ea-92ef-700f42ea503f.png)

The following diagram seeks to isolate `vector` dialects from the complexity
of the codegen paths and focus on the payload-carrying ops that operate on std
and `vector` types. This diagram is not to be taken as set in stone and
representative of what exists today but rather illustrates the layering of
abstractions in MLIR.

![`vector` Abstractions in MLIR](https://user-images.githubusercontent.com/10148468/71176949-e85ad000-2238-11ea-9806-200843bc4943.png)

This  separates concerns related to (a) defining efficient operations on
`vector` types from (b) program analyses + transformations on `memref`, loops
and other types of structured ops (be they `HLO`, `LHLO`, `Linalg` or other ).
Looking a bit forward in time, we can put a stake in the ground and venture
that the higher level of `vector`-level primitives we build and target from
codegen (or some user/language level), the simpler our task will be, the more
complex patterns can be expressed and the better performance will be.

## Components of a Generic Retargetable Vector-Level Dialect
The existing MLIR `vector`-level dialects are related to the following
bottom-up abstractions:

1. Representation in `LLVMIR` via data structures, instructions and
intrinsics. This is referred to as the `LLVM` level.
2. Set of machine-specific operations and types that are built to translate
almost 1-1 with the HW ISA. This is referred to as the Hardware Vector level;
a.k.a `HWV`. For instance, we have (a) the `NVVM` dialect (for `CUDA`) with
tensor core ops, (b) accelerator-specific dialects (internal), a potential
(future) `CPU` dialect to capture `LLVM` intrinsics more closely and other
dialects for specific hardware. Ideally this should be auto-generated as much
as possible from the `LLVM` level.
3. Set of virtual, machine-agnostic, operations that are informed by costs at
the `HWV`-level. This is referred to as the Virtual Vector level; a.k.a
`VV`. This is the level that higher-level abstractions (codegen, automatic
vectorization, potential vector language, ...) targets.

The existing generic, retargetable, `vector`-level dialect is related to the
following top-down rewrites and conversions:

1. MLIR Rewrite Patterns applied by the MLIR `PatternRewrite` infrastructure
to progressively lower to implementations that match closer and closer to the
`HWV`. Some patterns are "in-dialect" `VV -> VV` and some are conversions `VV
-> HWV`.
2. `Virtual Vector -> Hardware Vector` lowering is specified as a set of MLIR
lowering patterns that are specified manually for now.
3. `Hardware Vector -> LLVM` lowering is a mechanical process that is written
manually at the moment and that should be automated, following the `LLVM ->
Hardware Vector` ops generation as closely as possible.

## Short Description of the Existing Infrastructure

### LLVM level
On CPU, the `n-D` `vector` type currently lowers to
`!llvm<array<vector>>`. More concretely, `vector<4x8x128xf32>` lowers to
`!llvm<[4 x [ 8 x [ 128 x float ]]]>`.
There are tradeoffs involved related to how one can access subvectors and how
one uses `llvm.extractelement`, `llvm.insertelement` and
`llvm.shufflevector`. A [deeper dive section](#DeeperDive) discusses the
current lowering choices and tradeoffs.

### Hardware Vector Ops
Hardware Vector Ops are implemented as one dialect per target.
For internal hardware, we are auto-generating the specific HW dialects.
For `GPU`, the `NVVM` dialect adds operations such as `mma.sync`, `shfl` and
tests.
For `CPU` things are somewhat in-flight because the abstraction is close to
`LLVMIR`. The jury is still out on  whether a generic `CPU` dialect is
concretely needed, but it seems reasonable to have the same levels of
abstraction for all targets and perform cost-based lowering decisions in MLIR
even for `LLVM`.
Specialized `CPU` dialects that would capture specific features not well
captured by LLVM peephole optimizations of on different types that core MLIR
supports (e.g. Scalable Vectors) are welcome future extensions.

### Virtual Vector Ops
Some existing Standard and Vector Dialect on `n-D` `vector` types comprise:
```
%2 = std.addf %0, %1 : vector<3x7x8xf32>  // -> vector<3x7x8xf32>
%2 = std.mulf %0, %1 : vector<3x7x8xf32>  // -> vector<3x7x8xf32>
%2 = std.splat %1    : vector<3x7x8xf32>  // -> vector<3x7x8xf32>

%1 = vector.extract %0[1]: vector<3x7x8xf32>                 // -> vector<7x8xf32>
%1 = vector.extract %0[1, 5]: vector<3x7x8xf32>            // -> vector<8xf32>
%2 = vector.outerproduct %0, %1: vector<4xf32>, vector<8xf32>     // -> vector<4x8xf32>
%3 = vector.outerproduct %0, %1, %2: vector<4xf32>, vector<8xf32> // fma when adding %2
%3 = vector.strided_slice %0 {offsets = [2, 2], sizes = [2, 2], strides = [1, 1]}:
   vector<4x8x16xf32> // Returns a slice of type vector<2x2x16xf32>

%2 = vector.transfer_read %A[%0, %1]
  {permutation_map = (d0, d1) -> (d0)}: memref<7x?xf32>, vector<4xf32>

vector.transfer_write %f1, %A[%i0, %i1, %i2, %i3]
  {permutation_map = (d0, d1, d2, d3) -> (d3, d1, d0)} :
    vector<5x4x3xf32>, memref<?x?x?x?xf32>
```

The list of Vector is currently undergoing evolutions and is best kept
track of by following the evolution of the
[VectorOps.td](https://github.com/llvm/llvm-project/blob/master/mlir/include/mlir/Dialect/Vector/VectorOps.td)
ODS file (markdown documentation is automatically generated locally when
building and populates the [Vector
doc](https://github.com/llvm/llvm-project/blob/master/mlir/docs/Dialects/Vector.md)). Recent
extensions are driven by concrete use cases of interest. A notable such use
case is the `vector.contract` op which applies principles of the StructuredOps
abstraction to `vector` types.

### Virtual Vector Rewrite Patterns

The following rewrite patterns exist at the `VV->VV` level:

1. The now retired `MaterializeVector` pass used to legalize ops on a
coarse-grained virtual `vector` to a finer-grained virtual `vector` by
unrolling. This has been rewritten as a retargetable unroll-and-jam pattern on
`vector` ops and `vector` types.
2. The lowering of `vector_transfer` ops legalizes `vector` load/store ops to
permuted loops over scalar load/stores. This should evolve to loops over
`vector` load/stores + `mask` operations as they become available `vector` ops
at the `VV` level.

The general direction is to add more Virtual Vector level ops and implement
more useful `VV -> VV` rewrites as composable patterns that the PatternRewrite
infrastructure can apply iteratively.

### Virtual Vector to Hardware Vector Lowering
For now, `VV -> HWV`  are specified in C++ (see for instance the
[SplatOpLowering for n-D
vectors](https://github.com/tensorflow/mlir/commit/0a0c4867c6a6fcb0a2f17ef26a791c1d551fe33d)
or the [VectorOuterProductOp
lowering](https://github.com/tensorflow/mlir/commit/957b1ca9680b4aacabb3a480fbc4ebd2506334b8)).

Simple [conversion
tests](https://github.com/llvm/llvm-project/blob/master/mlir/test/Conversion/VectorToLLVM/vector-to-llvm.mlir)
are available for the `LLVM` target starting from the Virtual Vector Level.

## Rationale
### Hardware as `vector` Machines of Minimum Granularity

Higher-dimensional `vector`s are ubiquitous in modern HPC hardware. One way to
think about Generic Retargetable `vector`-Level Dialect is that it operates on
`vector` types that are a multiples of a "good" `vector` size so the HW can
efficiently implement a set of high-level primitives
(e.g. `vector<8x8x8x16xf32>` when HW `vector` size is say `vector<4x8xf32>`).

Some notable `vector` sizes of interest include:

1. CPU: `vector<HW_vector_size * k>`,  `vector<core_count * k’ x
HW_vector_size * k>` and  `vector<socket_count x core_count * k’ x
HW_vector_size * k>`
2. GPU: `vector<warp_size * k>`, `vector<warp_size * k  x float4>` and
`vector<warp_size * k x 4 x 4 x 4>` for tensor_core sizes,
3. Other accelerators:  n-D `vector` as first-class citizens in the HW.

Depending on the target, ops on sizes that are not multiples of the HW
`vector` size may either produce slow code (e.g. by going through `LLVM`
legalization) or may not legalize at all (e.g. some unsupported accelerator X
combination of ops and types).

### Transformations Problems Avoided
A `vector<16x32x64xf32>` virtual `vector` is a coarse-grained type that can be
“unrolled” to HW-specific sizes. The multi-dimensional unrolling factors are
carried in the IR by the `vector` type. After unrolling, traditional
instruction-level scheduling can be run.

The following key transformations (along with the supporting analyses and
structural constraints) are completely avoided by operating on a ``vector``
`ssa-value` abstraction:

1. Loop unroll and unroll-and-jam.
2. Loop and load-store restructuring for register reuse.
3. Load to store forwarding and Mem2reg.
4. Coarsening (raising) from finer-grained `vector` form.

Note that “unrolling” in the context of `vector`s corresponds to partial loop
unroll-and-jam and not full unrolling. As a consequence this is expected to
compose with SW pipelining where applicable and does not result in ICache blow
up.

### The Big Out-Of-Scope Piece: Automatic Vectorization
One important piece not discussed here is automatic vectorization
(automatically raising from scalar to n-D `vector` ops and types). The TL;DR
is that when the first "super-vectorization" prototype was implemented, MLIR
was nowhere near as mature as it is today. As we continue building more
abstractions in  `VV -> HWV`, there is an opportunity to revisit vectorization
in MLIR.

Since this topic touches on codegen abstractions, it is technically out of the
scope of this survey document but there is a lot to discuss in light of
structured op type representations and how a vectorization transformation can
be reused across dialects. In particular, MLIR allows the definition of
dialects at arbitrary levels of granularity and lends itself favorably to
progressive lowering. The argument can be made that automatic vectorization on
a loops + ops abstraction is akin to raising structural information that has
been lost. Instead, it is possible to revisit vectorization as simple pattern
rewrites, provided the IR is in a suitable form. For instance, vectorizing a
`linalg.generic` op whose semantics match a `matmul` can be done [quite easily
with a
pattern](https://github.com/tensorflow/mlir/commit/bff722d6b59ab99b998f0c2b9fccd0267d9f93b5). In
fact this pattern is trivial to generalize to any type of contraction when
targeting the `vector.contract` op, as well as to any field (`+/*`, `min/+`,
`max/+`, `or/and`, `logsumexp/+` ...) . In other words, by operating on a
higher level of generic abstractions than affine loops, non-trivial
transformations become significantly simpler and composable at a finer
granularity.

Irrespective of the existence of an auto-vectorizer, one can build a notional
vector language based on the VectorOps dialect and build end-to-end models
with expressing `vector`s in the IR directly and simple
pattern-rewrites. [EDSC](https://github.com/llvm/llvm-project/blob/master/mlir/docs/EDSC.md)s
provide a simple way of driving such a notional language directly in C++.

## Bikeshed Naming Discussion
There are arguments against naming an n-D level of abstraction `vector`
because most people associate it with 1-D `vector`s. On the other hand,
`vector`s are first-class n-D values in MLIR.
The alternative name Tile has been proposed, which conveys higher-D
meaning. But it also is one of the most overloaded terms in compilers and
hardware.
For now, we generally use the `n-D` `vector` name and are open to better
suggestions.

## DeeperDive

This section describes the tradeoffs involved in lowering the MLIR n-D vector
type and  operations on it to LLVM-IR. Putting aside the [LLVM
Matrix](http://lists.llvm.org/pipermail/llvm-dev/2018-October/126871.html)
proposal for now, this assumes LLVM only has built-in support for 1-D
vector. The relationship with the LLVM Matrix proposal is discussed at the end
of this document.

MLIR does not currently support dynamic vector sizes (i.e. SVE style) so the
discussion is limited to static rank and static vector sizes
(e.g. `vector<4x8x16x32xf32>`). This section discusses operations on vectors
in LLVM and MLIR.

LLVM instructions are prefixed by the `llvm.` dialect prefix
(e.g. `llvm.insertvalue`). Such ops operate exclusively on 1-D vectors and
aggregates following the [LLVM LangRef](https://llvm.org/docs/LangRef.html).
MLIR operations are prefixed by the `vector.` dialect prefix
(e.g. `vector.insertelement`). Such ops operate exclusively on MLIR `n-D`
`vector` types.

### Alternatives For Lowering an n-D Vector Type to LLVM
Consider a vector of rank n with  static sizes `{s_0, ... s_{n-1}}` (i.e. an
MLIR `vector<s_0x...s_{n-1}xf32>`). Lowering such an `n-D` MLIR vector type to
an LLVM descriptor can be done by either:

1. Flattening to a `1-D` vector: `!llvm<"(s_0*...*s_{n-1})xfloat">` in the
MLIR LLVM dialect.
2. Nested aggregate type of `1-D` vector:
`!llvm<"[s_0x[s_1x[...<s_{n-1}xfloat>]]]">` in the MLIR LLVM dialect.
3. A mix of both.

There are multiple tradeoffs involved in choosing one or the other that we
discuss. It is important to note that “a mix of both” immediately reduces to
“nested aggregate type of 1-D vector” with a `vector.cast %0:
vector<4x8x16x32xf32> to vector<4x4096xf32>` operation, that flattens the most
"k" minor dimensions.

### Constraints Inherited from LLVM (see LangRef)
The first constraint was already mentioned: LLVM only supports `1-D` `vector`
types natively.
Additional constraints are related to the difference in LLVM between vector
and aggregate types:
```
 “Aggregate Types are a subset of derived types that can contain multiple
 member types. Arrays and structs are aggregate types. Vectors are not
 considered to be aggregate types.”.
```

This distinction is also reflected in some of the operations. For `1-D`
vectors, the operations `llvm.extractelement`, `llvm.insertelement`, and
`llvm.shufflevector` apply, with direct support for dynamic indices. For `n-D`
vectors with `n>1`, and thus aggregate types at LLVM level, the more
restrictive operations `llvm.extractvalue` and `llvm.insertvalue` apply, which
only accept static indices. There is no direct shuffling support for aggregate
types.

The next sentence illustrates a recurrent tradeoff, also found in MLIR,
between “value types” (subject to SSA use-def chains) and “memory types”
(subject to aliasing and side-effects):
```
“Structures in memory are accessed using ‘load’ and ‘store’ by getting a
pointer to a field with the llvm.getelementptr instruction. Structures in
registers are accessed using the llvm.extractvalue and llvm.insertvalue
instructions.”
```

When transposing this to MLIR, `llvm.getelementptr` works on pointers to `n-D`
vectors in memory. For `n-D`, vectors values that live in registers we can use
`vector.extract` and `vector.insert` which do not accept dynamic indices. Note
that this is consistent with hardware considerations as discussed below.

An alternative is to use an LLVM `1-D` `vector` type for which one can use
`llvm.extractelement`, `llvm.insertelement` and `llvm.shufflevector`. These
operations accept dynamic indices. The implication is that one has to use a
flattened lowering of an MLIR n-D vector to an LLVM 1-D vector.

There are multiple tradeoffs involved that mix implications on the programming
model, execution on actual HW and what is visible or hidden from codegen. They
are discussed in the following sections.

### Nested Aggregate
Pros:

1. Natural encoding n-D vector -> (n-1)-D aggregate over 1-D vector.
2. No need for linearization / delinearization logic inserted everywhere.
3. `llvm.insertvalue`, `llvm.extractvalue` of `(n-k)-D` aggregate is natural.
4. `llvm.insertelement`, `llvm.extractelement`, `llvm.shufflevector` over
`1-D` vector type is natural.

Cons:

1. `llvm.insertvalue` / `llvm.extractvalue` does not accept dynamic indices
but only static ones.
2. Dynamic indexing on the non-most-minor dimension requires roundtrips to
memory.
3. Special intrinsics and native instructions in LLVM operate on `1-D`
vectors. This is not expected to be a practical limitation thanks to a
`vector.cast %0: vector<4x8x16x32xf32> to vector<4x4096xf32>` operation, that
flattens the most minor dimensions (see the bigger picture in implications on
codegen).

### Flattened 1-D Vector Type

Pros:

1. `insertelement` / `extractelement` / `shufflevector` with dynamic indexing
is possible over the whole lowered `n-D` vector type.
2. Supports special intrinsics and native operations.

Cons:
1. Requires linearization/delinearization logic everywhere, translations are
complex.
2. Hides away the real HW structure behind dynamic indexing: at the end of the
day, HW vector sizes are generally fixed and multiple vectors will be needed
to hold a vector that is larger than the HW.
3. Unlikely peephole optimizations will result in good code: arbitrary dynamic
accesses, especially at HW vector boundaries unlikely to result in regular
patterns.

### Discussion
#### HW Vectors and Implications on the SW and the Programming Model
As of today, the LLVM model only support `1-D` vector types. This is
unsurprising because historically, the vast majority of HW only supports `1-D`
vector registers. We note that multiple HW vendors are in the process of
evolving to higher-dimensional physical vectors.

In the following discussion, let's assume the HW vector size is `1-D` and the
SW vector size is `n-D`, with `n >= 1`. The same discussion would apply with
`2-D` HW `vector` size and `n >= 2`. In this context, most HW exhibit a vector
register file. The number of such vectors is fixed.
Depending on the rank and sizes of the SW vector abstraction and the HW vector
sizes and number of registers, an `n-D` SW vector type may be materialized by
a mix of multiple `1-D` HW vector registers + memory locations at a given
point in time.

The implication of the physical HW constraints on the programming model are
that one cannot index dynamically across hardware registers: a register file
can generally not be indexed dynamically. This is because the register number
is fixed and one either needs to unroll explicitly to obtain fixed register
numbers or go through memory. This is a constraint familiar to CUDA
programmers: when declaring a `private float a[4]`; and subsequently indexing
with a *dynamic* value results in so-called **local memory** usage
(i.e. roundtripping to memory).

#### Implication on codegen
MLIR `n-D` vector types are currently represented as `(n-1)-D` arrays of `1-D`
vectors when lowered to LLVM.
This introduces the consequences on static vs dynamic indexing discussed
previously: `extractelement`, `insertelement` and `shufflevector` on `n-D`
vectors in MLIR only support static indices. Dynamic indices are only
supported on the most minor `1-D` vector but not the outer `(n-1)-D`.
For other cases, explicit load / stores are required.

The implications on codegen are as follows:

1. Loops around `vector` values are indirect addressing of vector values, they
must operate on explicit load / store operations over `n-D` vector types.
2. Once an `n-D` `vector` type is loaded into an SSA value (that may or may
not live in `n` registers, with or without spilling, when eventually lowered),
it may be unrolled to smaller `k-D` `vector` types and operations that
correspond to the HW. This level of MLIR codegen is related to register
allocation and spilling that occur much later in the LLVM pipeline.
3. HW may support >1-D vectors with intrinsics for indirect addressing within
these vectors. These can be targeted thanks to explicit `vector_cast`
operations from MLIR `k-D` vector types and operations to LLVM `1-D` vectors +
intrinsics.

Alternatively, we argue that directly lowering to a linearized abstraction
hides away the codegen complexities related to memory accesses by giving a
false impression of magical dynamic indexing across registers. Instead we
prefer to make those very explicit in MLIR and allow codegen to explore
tradeoffs.
Different HW will require different tradeoffs in the sizes involved in steps
1., 2. and 3.

Decisions made at the MLIR level will have implications at a much later stage
in LLVM (after register allocation). We do not envision to expose concerns
related to modeling of register allocation and spilling to MLIR
explicitly. Instead, each target will expose a set of "good" target operations
and `n-D` vector types, associated with costs that `PatterRewriters` at the
MLIR level will be able to target. Such costs at the MLIR level will be
abstract and used for ranking, not for accurate performance modeling. In the
future such costs will be learned.

#### Implication on Lowering to Accelerators
To target accelerators that support higher dimensional vectors natively, we
can start from either `1-D` or `n-D` vectors in MLIR and use `vector.cast` to
flatten the most minor dimensions to `1-D` `vector<Kxf32>` where `K` is an
appropriate constant. Then, the existing lowering to LLVM-IR immediately
applies, with extensions for accelerator-specific intrinsics.

It is the role of an Accelerator-specific vector dialect (see codegen flow in
the figure above) to lower the `vector.cast`. Accelerator -> LLVM lowering
would then consist of a bunch of `Accelerator -> Accelerator` rewrites to
perform the casts composed with `Accelerator -> LLVM` conversions + intrinsics
that operate on `1-D` `vector<Kxf32>`.

Some of those rewrites may need extra handling, especially if a reduction is
involved. For example, `vector.cast %0: vector<K1x...xKnxf32> to
vector<Kxf32>` when `K != K1 * … * Kn` and some arbitrary irregular
`vector.cast %0: vector<4x4x17xf32> to vector<Kxf32>` may introduce masking
and intra-vector shuffling that may not be worthwhile or even feasible,
i.e. infinite cost.

However `vector.cast %0: vector<K1x...xKnxf32> to vector<Kxf32>` when `K =
K1 * … * Kn` should be close to a noop.

As we start building accelerator-specific abstractions, we hope to achieve
retargetable codegen: the same infra is used for CPU, GPU and accelerators
with extra MLIR patterns and costs.

#### Implication on calling external functions that operate on vectors
It is possible (likely) that we additionally need to linearize when calling an
external function.

### Relationship to LLVM matrix type proposal.
The LLVM matrix proposal was formulated 1 year ago but seemed to be somewhat
stalled until recently. In its current form, it is limited to 2-D matrix types
and operations are implemented with LLVM intrinsics.
In contrast, MLIR sits at a higher level of abstraction and allows the
lowering of generic operations on generic n-D vector types from MLIR to
aggregates of 1-D LLVM vectors.
In the future, it could make sense to lower to the LLVM matrix abstraction
also for CPU even though MLIR will continue needing higher level abstractions.

On the other hand, one should note that as MLIR is moving to LLVM, this
document could become the unifying abstraction that people should target for
>1-D vectors and the LLVM matrix proposal can be viewed as a subset of this
work.

### Conclusion
The flattened 1-D vector design in the LLVM matrix proposal is good in a
HW-specific world with special intrinsics. This is a good abstraction for
register allocation, Instruction-Level-Parallelism and
SoftWare-Pipelining/Modulo Scheduling optimizations at the register level.
However MLIR codegen operates at a higher level of abstraction where we want
to target operations on coarser-grained vectors than the HW size and on which
unroll-and-jam is applied and patterns across multiple HW vectors can be
matched.

This makes “nested aggregate type of 1-D vector” an appealing abstraction for
lowering from MLIR because:

1. it does not hide complexity related to the buffer vs value semantics and
the memory subsystem and
2. it does not rely on LLVM to magically make all the things work from a too
low-level abstraction.

The use of special intrinsics in a `1-D` LLVM world is still available thanks
to an explicit `vector.cast` op.

## Operations

[include "Dialects/VectorOps.md"]