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llvm-project/mlir/lib/Transforms/Vectorize.cpp

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//===- Vectorize.cpp - Vectorize Pass Impl ----------------------*- C++ -*-===//
//
// Copyright 2019 The MLIR Authors.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// =============================================================================
//
// This file implements vectorization of loops, operations and data types to
// a target-independent, n-D super-vector abstraction.
//
//===----------------------------------------------------------------------===//
#include "mlir/Analysis/LoopAnalysis.h"
#include "mlir/Analysis/MLFunctionMatcher.h"
#include "mlir/Analysis/VectorAnalysis.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/Location.h"
#include "mlir/IR/MLValue.h"
#include "mlir/IR/SSAValue.h"
#include "mlir/IR/Types.h"
#include "mlir/Pass.h"
#include "mlir/StandardOps/StandardOps.h"
#include "mlir/SuperVectorOps/SuperVectorOps.h"
#include "mlir/Support/Functional.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/Passes.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/DenseSet.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallString.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
using namespace mlir;
///
/// Implements a high-level vectorization strategy on an MLFunction.
/// The abstraction used is that of super-vectors, which provide a single,
/// compact, representation in the vector types, information that is expected
/// to reduce the impact of the phase ordering problem
///
/// Vector granularity:
/// ===================
/// This pass is designed to perform vectorization at a super-vector
/// granularity. A super-vector is loosely defined as a vector type that is a
/// multiple of a "good" vector size so the HW can efficiently implement a set
/// of high-level primitives. Multiple is understood along any dimension; e.g.
/// both vector<16xf32> and vector<2x8xf32> are valid super-vectors for a
/// vector<8xf32> HW vector. Note that a "good vector size so the HW can
/// efficiently implement a set of high-level primitives" is not necessarily an
/// integer multiple of actual hardware registers. We leave details of this
/// distinction unspecified for now.
///
/// Some may prefer the terminology a "tile of HW vectors". In this case, one
/// should note that super-vectors implement an "always full tile" abstraction.
/// They guarantee no partial-tile separation is necessary by relying on a
/// high-level copy-reshape abstraction that we call vector_transfer. This
/// copy-reshape operations is also responsible for performing layout
/// transposition if necessary. In the general case this will require a scoped
/// allocation in some notional local memory.
///
/// Whatever the mental model one prefers to use for this abstraction, the key
/// point is that we burn into a single, compact, representation in the vector
/// types, information that is expected to reduce the impact of the phase
/// ordering problem. Indeed, a vector type conveys information that:
/// 1. the associated loops have dependency semantics that do not prevent
/// vectorization;
/// 2. the associate loops have been sliced in chunks of static sizes that are
/// compatible with vector sizes (i.e. similar to unroll-and-jam);
/// 3. the inner loops, in the unroll-and-jam analogy of 2, are captured by
/// the
/// vector type and no vectorization hampering transformations can be
/// applied to them anymore;
/// 4. the underlying memrefs are accessed in some notional contiguous way
/// that allows loading into vectors with some amount of spatial locality;
/// In other words, super-vectorization provides a level of separation of
/// concern by way of opacity to subsequent passes. This has the effect of
/// encapsulating and propagating vectorization constraints down the list of
/// passes until we are ready to lower further.
///
/// For a particular target, a notion of minimal n-d vector size will be
/// specified and vectorization targets a multiple of those. In the following
/// paragraph, let "k ." represent "a multiple of", to be understood as a
/// multiple in the same dimension (e.g. vector<16 x k . 128> summarizes
/// vector<16 x 128>, vector<16 x 256>, vector<16 x 1024>, etc).
///
/// Some non-exhaustive notable super-vector sizes of interest include:
/// - CPU: vector<k . HW_vector_size>,
/// vector<k' . core_count x k . HW_vector_size>,
/// vector<socket_count x k' . core_count x k . HW_vector_size>;
/// - GPU: vector<k . warp_size>,
/// vector<k . warp_size x float2>,
/// vector<k . warp_size x float4>,
/// vector<k . warp_size x 4 x 4x 4> (for tensor_core sizes).
///
/// Loops and operations are emitted that operate on those super-vector shapes.
/// Subsequent lowering passes will materialize to actual HW vector sizes. These
/// passes are expected to be (gradually) more target-specific.
///
/// At a high level, a vectorized load in a loop will resemble:
/// ```mlir
/// for %i = ? to ? step ? {
/// %v_a = "vector_transfer_read" (A, %i) : (memref<?xf32>, index) ->
/// vector<128xf32>
/// }
/// ```
/// It is the reponsibility of the implementation of the vector_transfer_read
/// to materialize vector registers from the original scalar memrefs.
/// A later (more target-dependent) lowering pass will materialize to actual HW
/// vector sizes. This lowering may be occur at different times:
/// 1. at the MLIR level into a combination of loops, unrolling, DmaStartOp +
/// DmaWaitOp + vectorized operations
/// for data transformations and shuffle; thus opening opportunities for
/// unrolling and pipelining. This is an instance of library call
/// "whiteboxing"; or
/// 2. later in the a target-specific lowering pass or hand-written library
/// call; achieving full separation of concerns. This is an instance of
/// library call; or
/// 3. a mix of both, e.g. based on a model.
/// In the future, these operations will expose a contract to constrain the
/// search on vectorization patterns and sizes.
///
/// Occurrence of super-vectorization in the compiler flow:
/// =======================================================
/// This is an active area of investigation. We start with 2 remarks to position
/// super-vectorization in the context of existing ongoing work: LLVM VPLAN
/// and LLVM SLP Vectorizer.
///
/// LLVM VPLAN:
/// -----------
/// The astute reader may have noticed that in the limit, super-vectorization
/// can be applied at a similar time and with similar objectives than VPLAN.
/// For instance, in the case of a traditional, polyhedral compilation-flow (for
/// instance, the PPCG project uses ISL to provide dependence analysis,
/// multi-level(scheduling + tiling), lifting footprint to fast memory,
/// communication synthesis, mapping, register optimizations) and before
/// unrolling. When vectorization is applied at this *late* level in a typical
/// polyhedral flow, and is instantiated with actual hardware vector sizes,
/// super-vectorization is expected to match (or subsume) the type of patterns
/// that LLVM's VPLAN aims at targeting. The main difference here is that MLIR
/// is higher level and our implementation should be significantly simpler. Also
/// note that in this mode, recursive patterns are probably a bit of an overkill
/// although it is reasonable to expect that mixing a bit of outer loop and
/// inner loop vectorization + unrolling will provide interesting choices to
/// MLIR.
///
/// LLVM SLP Vectorizer:
/// --------------------
/// Super-vectorization however is not meant to be usable in a similar fashion
/// to the SLP vectorizer. The main difference lies in the information that
/// both vectorizers use: super-vectorization examines contiguity of memory
/// references along fastest varying dimensions and loops with recursive nested
/// patterns capturing imperfectly-nested loop nests; the SLP vectorizer, on
/// the other hand, performs flat pattern matching inside a single unrolled loop
/// body and stitches together pieces of load and store instructions into full
/// 1-D vectors. We envision that the SLP vectorizer is a good way to capture
/// innermost loop, control-flow dependent patterns that super-vectorization may
/// not be able to capture easily. In other words, super-vectorization does not
/// aim at replacing the SLP vectorizer and the two solutions are complementary.
///
/// Ongoing investigations:
/// -----------------------
/// We discuss the following *early* places where super-vectorization is
/// applicable and touch on the expected benefits and risks . We list the
/// opportunities in the context of the traditional polyhedral compiler flow
/// described in PPCG. There are essentially 6 places in the MLIR pass pipeline
/// we expect to experiment with super-vectorization:
/// 1. Right after language lowering to MLIR: this is the earliest time where
/// super-vectorization is expected to be applied. At this level, all the
/// language/user/library-level annotations are available and can be fully
/// exploited. Examples include loop-type annotations (such as parallel,
/// reduction, scan, dependence distance vector, vectorizable) as well as
/// memory access annotations (such as non-aliasing writes guaranteed,
/// indirect accesses that are permutations by construction) accesses or
/// that a particular operation is prescribed atomic by the user. At this
/// level, anything that enriches what dependence analysis can do should be
/// aggressively exploited. At this level we are close to having explicit
/// vector types in the language, except we do not impose that burden on the
/// programmer/library: we derive information from scalar code + annotations.
/// 2. After dependence analysis and before polyhedral scheduling: the
/// information that supports vectorization does not need to be supplied by a
/// higher level of abstraction. Traditional dependence anaysis is available
/// in MLIR and will be used to drive vectorization and cost models.
///
/// Let's pause here and remark that applying super-vectorization as described
/// in 1. and 2. presents clear opportunities and risks:
/// - the opportunity is that vectorization is burned in the type system and
/// is protected from the adverse effect of loop scheduling, tiling, loop
/// interchange and all passes downstream. Provided that subsequent passes are
/// able to operate on vector types; the vector shapes, associated loop
/// iterator properties, alignment, and contiguity of fastest varying
/// dimensions are preserved until we lower the super-vector types. We expect
/// this to significantly rein in on the adverse effects of phase ordering.
/// - the risks are that a. all passes after super-vectorization have to work
/// on elemental vector types (not that this is always true, wherever
/// vectorization is applied) and b. that imposing vectorization constraints
/// too early may be overall detrimental to loop fusion, tiling and other
/// transformations because the dependence distances are coarsened when
/// operating on elemental vector types. For this reason, the pattern
/// profitability analysis should include a component that also captures the
/// maximal amount of fusion available under a particular pattern. This is
/// still at the stage of rought ideas but in this context, search is our
/// friend as the Tensor Comprehensions and auto-TVM contributions
/// demonstrated previously.
/// Bottom-line is we do not yet have good answers for the above but aim at
/// making it easy to answer such questions.
///
/// Back to our listing, the last places where early super-vectorization makes
/// sense are:
/// 3. right after polyhedral-style scheduling: PLUTO-style algorithms are known
/// to improve locality, parallelism and be configurable (e.g. max-fuse,
/// smart-fuse etc). They can also have adverse effects on contiguity
/// properties that are required for vectorization but the vector_transfer
/// copy-reshape-pad-transpose abstraction is expected to help recapture
/// these properties.
/// 4. right after polyhedral-style scheduling+tiling;
/// 5. right after scheduling+tiling+rescheduling: points 4 and 5 represent
/// probably the most promising places because applying tiling achieves a
/// separation of concerns that allows rescheduling to worry less about
/// locality and more about parallelism and distribution (e.g. min-fuse).
///
/// At these levels the risk-reward looks different: on one hand we probably
/// lost a good deal of language/user/library-level annotation; on the other
/// hand we gained parallelism and locality through scheduling and tiling.
/// However we probably want to ensure tiling is compatible with the
/// full-tile-only abstraction used in super-vectorization or suffer the
/// consequences. It is too early to place bets on what will win but we expect
/// super-vectorization to be the right abstraction to allow exploring at all
/// these levels. And again, search is our friend.
///
/// Lastly, we mention it again here:
/// 6. as a MLIR-based alternative to VPLAN.
///
/// Lowering, unrolling, pipelining:
/// ================================
/// TODO(ntv): point to the proper places.
///
/// Algorithm:
/// ==========
/// The algorithm proceeds in a few steps:
/// 1. defining super-vectorization patterns and matching them on the tree of
/// ForStmt. A super-vectorization pattern is defined as a recursive data
/// structures that matches and captures nested, imperfectly-nested loops
/// that have a. comformable loop annotations attached (e.g. parallel,
/// reduction, vectoriable, ...) as well as b. all contiguous load/store
/// operations along a specified minor dimension (not necessarily the
/// fastest varying) ;
/// 2. analyzing those patterns for profitability (TODO(ntv): and
/// interference);
/// 3. Then, for each pattern in order:
/// a. applying iterative rewriting of the loop and the load operations in
/// DFS postorder. Rewriting is implemented by coarsening the loops and
/// turning load operations into opaque vector_transfer_read ops;
/// b. keeping track of the load operations encountered as "roots" and the
/// store operations as "terminators";
/// c. traversing the use-def chains starting from the roots and iteratively
/// propagating vectorized values. Scalar values that are encountered
/// during this process must come from outside the scope of the current
/// pattern (TODO(ntv): enforce this and generalize). Such a scalar value
/// is vectorized only if it is a constant (into a vector splat). The
/// non-constant case is not supported for now and results in the pattern
/// failing to vectorize;
/// d. performing a second traversal on the terminators (store ops) to
/// rewriting the scalar value they write to memory into vector form.
/// If the scalar value has been vectorized previously, we simply replace
/// it by its vector form. Otherwise, if the scalar value is a constant,
/// it is vectorized into a splat. In all other cases, vectorization for
/// the pattern currently fails.
/// e. if everything under the root ForStmt in the current pattern vectorizes
/// properly, we commit that loop to the IR. Otherwise we discard it and
/// restore a previously cloned version of the loop. Thanks to the
/// recursive scoping nature of matchers and captured patterns, this is
/// transparently achieved by a simple RAII implementation.
/// f. vectorization is applied on the next pattern in the list. Because
/// pattern interference avoidance is not yet implemented and that we do
/// not support further vectorizing an already vector load we need to
/// re-verify that the pattern is still vectorizable. This is expected to
/// make cost models more difficult to write and is subject to improvement
/// in the future.
///
/// Points c. and d. above are worth additional comment. In most passes that
/// do not change the type of operands, it is usually preferred to eagerly
/// `replaceAllUsesWith`. Unfortunately this does not work for vectorization
/// because during the use-def chain traversal, all the operands of an operation
/// must be available in vector form. Trying to propagate eagerly makes the IR
/// temporarily invalid and results in errors such as:
/// `vectorize.mlir:308:13: error: 'addf' op requires the same type for all
/// operands and results
/// %s5 = addf %a5, %b5 : f32`
///
/// Lastly, we show a minimal example for which use-def chains rooted in load /
/// vector_transfer_read are not enough. This is what motivated splitting
/// terminator processing out of the use-def chains starting from loads. In the
/// following snippet, there is simply no load::
/// ```mlir
/// mlfunc @fill(%A : memref<128xf32>) -> () {
/// %f1 = constant 1.0 : f32
/// for %i0 = 0 to 32 {
/// store %f1, %A[%i0] : memref<128xf32, 0>
/// }
/// return
/// }
/// ```
///
/// Choice of loop transformation to support the algorithm:
/// =======================================================
/// The choice of loop transformation to apply for coarsening vectorized loops
/// is still subject to exploratory tradeoffs. In particular, say we want to
/// vectorize by a factor 128, we want to transform the following input:
/// ```mlir
/// for %i = %M to %N {
/// %a = load A[%i] : memref<?xf32>
/// }
/// ```
///
/// Traditionally, one would vectorize late (after scheduling, tiling,
/// memory promotion etc) say after stripmining (and potentially unrolling in
/// the case of LLVM's SLP vectorizer):
/// ```mlir
/// for %i = floor(%M, 128) to ceil(%N, 128) {
/// for %ii = max(%M, 128 * %i) to min(%N, 128*%i + 127) {
/// %a = load A[%ii] : memref<?xf32>
/// }
/// }
/// ```
///
/// Instead, we seek to vectorize early and freeze vector types before
/// scheduling, so we want to generate a pattern that resembles:
/// ```mlir
/// for %i = ? to ? step ? {
/// %v_a = "vector_transfer_read" (A, %i) : (memref<?xf32>, index) ->
/// vector<128xf32>
/// }
/// ```
///
/// i. simply dividing the lower / upper bounds by 128 creates issues
/// when representing expressions such as ii + 1 because now we only
/// have access to original values that have been divided. Additional
/// information is needed to specify accesses at below-128 granularity;
/// ii. another alternative is to coarsen the loop step but this may have
/// consequences on dependence analysis and fusability of loops: fusable
/// loops probably need to have the same step (because we don't want to
/// stripmine/unroll to enable fusion).
/// As a consequence, we choose to represent the coarsening using the loop
/// step for now and reevaluate in the future. Note that we can renormalize
/// loop steps later if/when we have evidence that they are problematic.
///
/// For the simple strawman example above, vectorizing for a 1-D vector
/// abstraction of size 128 returns code similar to:
/// ```mlir
/// for %i = %M to %N step 128 {
/// %v_a = "vector_transfer_read" (A, %i) : (memref<?xf32>, index) ->
/// vector<128xf32>
/// }
/// ```
///
/// Unsupported cases, extensions, and work in progress (help welcome :-) ):
/// ========================================================================
/// 1. lowering to concrete vector types for various HW;
/// 2. reduction support;
/// 3. non-effecting padding during vector_transfer_read and filter during
/// vector_transfer_write;
/// 4. misalignment support vector_transfer_read / vector_transfer_write
/// (hopefully without read-modify-writes);
/// 5. control-flow support;
/// 6. cost-models, heuristics and search;
/// 7. Op implementation, extensions and implication on memref views;
/// 8. many TODOs left around.
///
/// Examples:
/// =========
/// Consider the following MLFunction:
/// ```mlir
/// mlfunc @vector_add_2d(%M : index, %N : index) -> f32 {
/// %A = alloc (%M, %N) : memref<?x?xf32, 0>
/// %B = alloc (%M, %N) : memref<?x?xf32, 0>
/// %C = alloc (%M, %N) : memref<?x?xf32, 0>
/// %f1 = constant 1.0 : f32
/// %f2 = constant 2.0 : f32
/// for %i0 = 0 to %M {
/// for %i1 = 0 to %N {
/// // non-scoped %f1
/// store %f1, %A[%i0, %i1] : memref<?x?xf32, 0>
/// }
/// }
/// for %i2 = 0 to %M {
/// for %i3 = 0 to %N {
/// // non-scoped %f2
/// store %f2, %B[%i2, %i3] : memref<?x?xf32, 0>
/// }
/// }
/// for %i4 = 0 to %M {
/// for %i5 = 0 to %N {
/// %a5 = load %A[%i4, %i5] : memref<?x?xf32, 0>
/// %b5 = load %B[%i4, %i5] : memref<?x?xf32, 0>
/// %s5 = addf %a5, %b5 : f32
/// // non-scoped %f1
/// %s6 = addf %s5, %f1 : f32
/// // non-scoped %f2
/// %s7 = addf %s5, %f2 : f32
/// // diamond dependency.
/// %s8 = addf %s7, %s6 : f32
/// store %s8, %C[%i4, %i5] : memref<?x?xf32, 0>
/// }
/// }
/// %c7 = constant 7 : index
/// %c42 = constant 42 : index
/// %res = load %C[%c7, %c42] : memref<?x?xf32, 0>
/// return %res : f32
/// }
/// ```
///
/// TODO(ntv): update post b/119731251.
/// The -vectorize pass with the following arguments:
/// ```
/// -vectorize -virtual-vector-size 256 --test-fastest-varying=0
/// ```
///
/// produces this standard innermost-loop vectorized code:
/// ```mlir
/// mlfunc @vector_add_2d(%arg0 : index, %arg1 : index) -> f32 {
/// %0 = alloc(%arg0, %arg1) : memref<?x?xf32>
/// %1 = alloc(%arg0, %arg1) : memref<?x?xf32>
/// %2 = alloc(%arg0, %arg1) : memref<?x?xf32>
/// %cst = constant 1.0 : f32
/// %cst_0 = constant 2.0 : f32
/// for %i0 = 0 to %arg0 {
/// for %i1 = 0 to %arg1 step 256 {
/// %cst_1 = constant splat<vector<256xf32>, 1.0> :
/// vector<256xf32>
/// "vector_transfer_write"(%cst_1, %0, %i0, %i1) :
/// (vector<256xf32>, memref<?x?xf32>, index, index) -> ()
/// }
/// }
/// for %i2 = 0 to %arg0 {
/// for %i3 = 0 to %arg1 step 256 {
/// %cst_2 = constant splat<vector<256xf32>, 2.0> :
/// vector<256xf32>
/// "vector_transfer_write"(%cst_2, %1, %i2, %i3) :
/// (vector<256xf32>, memref<?x?xf32>, index, index) -> ()
/// }
/// }
/// for %i4 = 0 to %arg0 {
/// for %i5 = 0 to %arg1 step 256 {
/// %3 = "vector_transfer_read"(%0, %i4, %i5) :
/// (memref<?x?xf32>, index, index) -> vector<256xf32>
/// %4 = "vector_transfer_read"(%1, %i4, %i5) :
/// (memref<?x?xf32>, index, index) -> vector<256xf32>
/// %5 = addf %3, %4 : vector<256xf32>
/// %cst_3 = constant splat<vector<256xf32>, 1.0> :
/// vector<256xf32>
/// %6 = addf %5, %cst_3 : vector<256xf32>
/// %cst_4 = constant splat<vector<256xf32>, 2.0> :
/// vector<256xf32>
/// %7 = addf %5, %cst_4 : vector<256xf32>
/// %8 = addf %7, %6 : vector<256xf32>
/// "vector_transfer_write"(%8, %2, %i4, %i5) :
/// (vector<256xf32>, memref<?x?xf32>, index, index) -> ()
/// }
/// }
/// %c7 = constant 7 : index
/// %c42 = constant 42 : index
/// %9 = load %2[%c7, %c42] : memref<?x?xf32>
/// return %9 : f32
/// }
/// ```
///
/// TODO(ntv): update post b/119731251.
/// The -vectorize pass with the following arguments:
/// ```
/// -vectorize -virtual-vector-size 32 -virtual-vector-size 256
/// --test-fastest-varying=1 --test-fastest-varying=0
/// ```
///
/// produces this more insteresting mixed outer-innermost-loop vectorized code:
/// ```mlir
/// mlfunc @vector_add_2d(%arg0 : index, %arg1 : index) -> f32 {
/// %0 = alloc(%arg0, %arg1) : memref<?x?xf32>
/// %1 = alloc(%arg0, %arg1) : memref<?x?xf32>
/// %2 = alloc(%arg0, %arg1) : memref<?x?xf32>
/// %cst = constant 1.0 : f32
/// %cst_0 = constant 2.0 : f32
/// for %i0 = 0 to %arg0 step 32 {
/// for %i1 = 0 to %arg1 step 256 {
/// %cst_1 = constant splat<vector<32x256xf32>, 1.0> :
/// vector<32x256xf32>
/// "vector_transfer_write"(%cst_1, %0, %i0, %i1) :
/// (vector<32x256xf32>, memref<?x?xf32>, index, index) -> ()
/// }
/// }
/// for %i2 = 0 to %arg0 step 32 {
/// for %i3 = 0 to %arg1 step 256 {
/// %cst_2 = constant splat<vector<32x256xf32>, 2.0> :
/// vector<32x256xf32>
/// "vector_transfer_write"(%cst_2, %1, %i2, %i3) :
/// (vector<32x256xf32>, memref<?x?xf32>, index, index) -> ()
/// }
/// }
/// for %i4 = 0 to %arg0 step 32 {
/// for %i5 = 0 to %arg1 step 256 {
/// %3 = "vector_transfer_read"(%0, %i4, %i5) :
/// (memref<?x?xf32>, index, index) -> vector<32x256xf32>
/// %4 = "vector_transfer_read"(%1, %i4, %i5) :
/// (memref<?x?xf32>, index, index) -> vector<32x256xf32>
/// %5 = addf %3, %4 : vector<32x256xf32>
/// %cst_3 = constant splat<vector<32x256xf32>, 1.0> :
/// vector<32x256xf32>
/// %6 = addf %5, %cst_3 : vector<32x256xf32>
/// %cst_4 = constant splat<vector<32x256xf32>, 2.0> :
/// vector<32x256xf32>
/// %7 = addf %5, %cst_4 : vector<32x256xf32>
/// %8 = addf %7, %6 : vector<32x256xf32>
/// "vector_transfer_write"(%8, %2, %i4, %i5) :
/// (vector<32x256xf32>, memref<?x?xf32>, index, index) -> ()
/// }
/// }
/// %c7 = constant 7 : index
/// %c42 = constant 42 : index
/// %9 = load %2[%c7, %c42] : memref<?x?xf32>
/// return %9 : f32
/// }
/// ```
///
/// Of course, much more intricate n-D imperfectly-nested patterns can be
/// vectorized too and specified in a fully declarative fashion.
#define DEBUG_TYPE "early-vect"
using functional::apply;
using functional::makePtrDynCaster;
using functional::map;
using functional::ScopeGuard;
using llvm::dbgs;
using llvm::DenseSet;
using llvm::SetVector;
static llvm::cl::list<int> clVirtualVectorSize(
"virtual-vector-size",
llvm::cl::desc("Specify n-D virtual vector size for early vectorization"),
llvm::cl::ZeroOrMore);
static llvm::cl::list<int> clFastestVaryingPattern(
"test-fastest-varying",
llvm::cl::desc(
"Specify a 1-D, 2-D or 3-D pattern of fastest varying memory"
" dimensions to match. See defaultPatterns in Vectorize.cpp for a"
" description and examples. This is used for testing purposes"),
llvm::cl::ZeroOrMore);
/// Forward declaration.
static FilterFunctionType
isVectorizableLoopPtrFactory(unsigned fastestVaryingMemRefDimension);
// Build a bunch of predetermined patterns that will be traversed in order.
// Due to the recursive nature of MLFunctionMatchers, this captures
// arbitrarily nested pairs of loops at any position in the tree.
/// Note that this currently only matches 2 nested loops and will be extended.
// TODO(ntv): support 3-D loop patterns with a common reduction loop that can
// be matched to GEMMs.
static std::vector<MLFunctionMatcher> defaultPatterns() {
using matcher::For;
return std::vector<MLFunctionMatcher>{
// 3-D patterns
For(isVectorizableLoopPtrFactory(2),
For(isVectorizableLoopPtrFactory(1),
For(isVectorizableLoopPtrFactory(0)))),
// for i { for j { A[??f(not i, not j), f(i, not j), f(not i, j)];}}
// test independently with:
// --test-fastest-varying=1 --test-fastest-varying=0
For(isVectorizableLoopPtrFactory(1),
For(isVectorizableLoopPtrFactory(0))),
// for i { for j { A[??f(not i, not j), f(i, not j), ?, f(not i, j)];}}
// test independently with:
// --test-fastest-varying=2 --test-fastest-varying=0
For(isVectorizableLoopPtrFactory(2),
For(isVectorizableLoopPtrFactory(0))),
// for i { for j { A[??f(not i, not j), f(i, not j), ?, ?, f(not i, j)];}}
// test independently with:
// --test-fastest-varying=3 --test-fastest-varying=0
For(isVectorizableLoopPtrFactory(3),
For(isVectorizableLoopPtrFactory(0))),
// for i { for j { A[??f(not i, not j), f(not i, j), f(i, not j)];}}
// test independently with:
// --test-fastest-varying=0 --test-fastest-varying=1
For(isVectorizableLoopPtrFactory(0),
For(isVectorizableLoopPtrFactory(1))),
// for i { for j { A[??f(not i, not j), f(not i, j), ?, f(i, not j)];}}
// test independently with:
// --test-fastest-varying=0 --test-fastest-varying=2
For(isVectorizableLoopPtrFactory(0),
For(isVectorizableLoopPtrFactory(2))),
// for i { for j { A[??f(not i, not j), f(not i, j), ?, ?, f(i, not j)];}}
// test independently with:
// --test-fastest-varying=0 --test-fastest-varying=3
For(isVectorizableLoopPtrFactory(0),
For(isVectorizableLoopPtrFactory(3))),
// for i { A[??f(not i) , f(i)];}
// test independently with: --test-fastest-varying=0
For(isVectorizableLoopPtrFactory(0)),
// for i { A[??f(not i) , f(i), ?];}
// test independently with: --test-fastest-varying=1
For(isVectorizableLoopPtrFactory(1)),
// for i { A[??f(not i) , f(i), ?, ?];}
// test independently with: --test-fastest-varying=2
For(isVectorizableLoopPtrFactory(2)),
// for i { A[??f(not i) , f(i), ?, ?, ?];}
// test independently with: --test-fastest-varying=3
For(isVectorizableLoopPtrFactory(3))};
}
/// Creates a vectorization pattern from the command line arguments.
/// Up to 3-D patterns are supported.
/// If the command line argument requests a pattern of higher order, returns an
/// empty pattern list which will conservatively result in no vectorization.
static std::vector<MLFunctionMatcher> makePatterns() {
using matcher::For;
if (clFastestVaryingPattern.empty()) {
return defaultPatterns();
}
switch (clFastestVaryingPattern.size()) {
case 1:
return {For(isVectorizableLoopPtrFactory(clFastestVaryingPattern[0]))};
case 2:
return {For(isVectorizableLoopPtrFactory(clFastestVaryingPattern[0]),
For(isVectorizableLoopPtrFactory(clFastestVaryingPattern[1])))};
case 3:
return {For(
isVectorizableLoopPtrFactory(clFastestVaryingPattern[0]),
For(isVectorizableLoopPtrFactory(clFastestVaryingPattern[1]),
For(isVectorizableLoopPtrFactory(clFastestVaryingPattern[2]))))};
default:
return std::vector<MLFunctionMatcher>();
}
}
namespace {
struct Vectorize : public FunctionPass {
Vectorize() : FunctionPass(&Vectorize::passID) {}
PassResult runOnMLFunction(MLFunction *f) override;
// Thread-safe RAII contexts local to pass, BumpPtrAllocator freed on exit.
MLFunctionMatcherContext MLContext;
static char passID;
};
} // end anonymous namespace
char Vectorize::passID = 0;
/////// TODO(ntv): Hoist to a VectorizationStrategy.cpp when appropriate. //////
namespace {
struct VectorizationStrategy {
ArrayRef<int> vectorSizes;
DenseMap<ForStmt *, unsigned> loopToVectorDim;
};
} // end anonymous namespace
static void vectorizeLoopIfProfitable(ForStmt *loop, unsigned depthInPattern,
unsigned patternDepth,
VectorizationStrategy *strategy) {
assert(patternDepth > depthInPattern &&
"patternDepth is greater than depthInPattern");
if (patternDepth - depthInPattern > strategy->vectorSizes.size()) {
// Don't vectorize this loop
return;
}
strategy->loopToVectorDim[loop] =
strategy->vectorSizes.size() - (patternDepth - depthInPattern);
}
/// Implements a simple strawman strategy for vectorization.
/// Given a matched pattern `matches` of depth `patternDepth`, this strategy
/// greedily assigns the fastest varying dimension ** of the vector ** to the
/// innermost loop in the pattern.
/// When coupled with a pattern that looks for the fastest varying dimension in
/// load/store MemRefs, this creates a generic vectorization strategy that works
/// for any loop in a hierarchy (outermost, innermost or intermediate).
///
/// TODO(ntv): In the future we should additionally increase the power of the
/// profitability analysis along 3 directions:
/// 1. account for loop extents (both static and parametric + annotations);
/// 2. account for data layout permutations;
/// 3. account for impact of vectorization on maximal loop fusion.
/// Then we can quantify the above to build a cost model and search over
/// strategies.
static bool analyzeProfitability(MLFunctionMatches matches,
unsigned depthInPattern, unsigned patternDepth,
VectorizationStrategy *strategy) {
for (auto m : matches) {
auto *loop = cast<ForStmt>(m.first);
bool fail = analyzeProfitability(m.second, depthInPattern + 1, patternDepth,
strategy);
if (fail) {
return fail;
}
vectorizeLoopIfProfitable(loop, depthInPattern, patternDepth, strategy);
}
return false;
}
///// end TODO(ntv): Hoist to a VectorizationStrategy.cpp when appropriate /////
namespace {
struct VectorizationState {
/// Adds an entry of pre/post vectorization statements in the state.
void registerReplacement(OperationStmt *key, OperationStmt *value);
/// When the current vectorization pattern is successful, this erases the
/// instructions that were marked for erasure in the proper order and resets
/// the internal state for the next pattern.
void finishVectorizationPattern();
// In-order tracking of original OperationStmt that have been vectorized.
// Erase in reverse order.
SmallVector<OperationStmt *, 16> toErase;
// Set of OperationStmt that have been vectorized (the values in the
// vectorizationMap for hashed access). The vectorizedSet is used in
// particular to filter the statements that have already been vectorized by
// this pattern, when iterating over nested loops in this pattern.
DenseSet<OperationStmt *> vectorizedSet;
// Map of old scalar OperationStmt to new vectorized OperationStmt.
DenseMap<OperationStmt *, OperationStmt *> vectorizationMap;
// Map of old scalar MLValue to new vectorized MLValue.
DenseMap<const MLValue *, MLValue *> replacementMap;
// The strategy drives which loop to vectorize by which amount.
const VectorizationStrategy *strategy;
// Use-def roots. These represent the starting points for the worklist in the
// vectorizeOperations function. They consist of the subset of load operations
// that have been vectorized. They can be retrieved from `vectorizationMap`
// but it is convenient to keep track of them in a separate data structure.
DenseSet<OperationStmt *> roots;
// Terminator statements for the worklist in the vectorizeOperations function.
// They consist of the subset of store operations that have been vectorized.
// They can be retrieved from `vectorizationMap` but it is convenient to keep
// track of them in a separate data structure. Since they do not necessarily
// belong to use-def chains starting from loads (e.g storing a constant), we
// need to handle them in a post-pass.
DenseSet<OperationStmt *> terminators;
// Checks that the type of `stmt` is StoreOp and adds it to the terminators
// set.
void registerTerminator(OperationStmt *stmt);
private:
void registerReplacement(const SSAValue *key, SSAValue *value);
};
} // end namespace
void VectorizationState::registerReplacement(OperationStmt *key,
OperationStmt *value) {
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ commit vectorized op: ");
LLVM_DEBUG(key->print(dbgs()));
LLVM_DEBUG(dbgs() << " into ");
LLVM_DEBUG(value->print(dbgs()));
assert(key->getNumResults() == 1 && "already registered");
assert(value->getNumResults() == 1 && "already registered");
assert(vectorizedSet.count(value) == 0 && "already registered");
assert(vectorizationMap.count(key) == 0 && "already registered");
toErase.push_back(key);
vectorizedSet.insert(value);
vectorizationMap.insert(std::make_pair(key, value));
registerReplacement(key->getResult(0), value->getResult(0));
if (key->isa<LoadOp>()) {
assert(roots.count(key) == 0 && "root was already inserted previously");
roots.insert(key);
}
}
void VectorizationState::registerTerminator(OperationStmt *stmt) {
assert(stmt->isa<StoreOp>() && "terminator must be a StoreOp");
assert(terminators.count(stmt) == 0 &&
"terminator was already inserted previously");
terminators.insert(stmt);
}
void VectorizationState::finishVectorizationPattern() {
while (!toErase.empty()) {
auto *stmt = toErase.pop_back_val();
LLVM_DEBUG(dbgs() << "\n[early-vect] finishVectorizationPattern erase: ");
LLVM_DEBUG(stmt->print(dbgs()));
stmt->erase();
}
}
void VectorizationState::registerReplacement(const SSAValue *key,
SSAValue *value) {
assert(replacementMap.count(cast<MLValue>(key)) == 0 &&
"replacement already registered");
replacementMap.insert(
std::make_pair(cast<MLValue>(key), cast<MLValue>(value)));
}
////// TODO(ntv): Hoist to a VectorizationMaterialize.cpp when appropriate. ////
/// Handles the vectorization of load and store MLIR operations.
///
/// LoadOp operations are the roots of the vectorizeOperations call. They are
/// vectorized immediately. The resulting vector_transfer_read is immediately
/// registered to replace all uses of the LoadOp in this pattern's scope.
///
/// StoreOp are the terminators of the vectorizeOperations call. They need
/// to be vectorized late once all the use-def chains have been traversed.
/// Additionally, they may have ssa-values operands which come from outside
/// the scope of the current pattern.
/// Such special cases force us to delay the vectorization of the stores
/// until the last step. Here we merely register the store operation.
template <typename LoadOrStoreOpPointer>
static bool vectorizeRootOrTerminal(MLValue *iv, LoadOrStoreOpPointer memoryOp,
VectorizationState *state) {
auto memRefType =
memoryOp->getMemRef()->getType().template cast<MemRefType>();
auto elementType = memRefType.getElementType();
// TODO(ntv): ponder whether we want to further vectorize a vector value.
assert(VectorType::isValidElementType(elementType) &&
"Not a valid vector element type");
auto vectorType = VectorType::get(state->strategy->vectorSizes, elementType);
// Materialize a MemRef with 1 vector.
auto *opStmt = cast<OperationStmt>(memoryOp->getOperation());
// For now, vector_transfers must be aligned, operate only on indices with an
// identity subset of AffineMap and do not change layout.
// TODO(ntv): increase the expressiveness power of vector_transfer operations
// as needed by various targets.
if (opStmt->template isa<LoadOp>()) {
auto permutationMap =
makePermutationMap(opStmt, state->strategy->loopToVectorDim);
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ permutationMap: ");
LLVM_DEBUG(permutationMap.print(dbgs()));
MLFuncBuilder b(opStmt);
auto transfer = b.create<VectorTransferReadOp>(
opStmt->getLoc(), vectorType, memoryOp->getMemRef(),
map(makePtrDynCaster<SSAValue>(), memoryOp->getIndices()),
permutationMap);
state->registerReplacement(opStmt,
cast<OperationStmt>(transfer->getOperation()));
} else {
state->registerTerminator(opStmt);
}
return false;
}
/// end TODO(ntv): Hoist to a VectorizationMaterialize.cpp when appropriate. ///
/// Coarsens the loops bounds and transforms all remaining load and store
/// operations into the appropriate vector_transfer.
static bool vectorizeForStmt(ForStmt *loop, int64_t step,
VectorizationState *state) {
using namespace functional;
loop->setStep(step);
FilterFunctionType notVectorizedThisPattern = [state](const Statement &stmt) {
if (!matcher::isLoadOrStore(stmt)) {
return false;
}
auto *opStmt = cast<OperationStmt>(&stmt);
return state->vectorizationMap.count(opStmt) == 0 &&
state->vectorizedSet.count(opStmt) == 0 &&
state->roots.count(opStmt) == 0 &&
state->terminators.count(opStmt) == 0;
};
auto loadAndStores = matcher::Op(notVectorizedThisPattern);
auto matches = loadAndStores.match(loop);
for (auto ls : matches) {
auto *opStmt = cast<OperationStmt>(ls.first);
auto load = opStmt->dyn_cast<LoadOp>();
auto store = opStmt->dyn_cast<StoreOp>();
LLVM_DEBUG(opStmt->print(dbgs()));
auto fail = load ? vectorizeRootOrTerminal(loop, load, state)
: vectorizeRootOrTerminal(loop, store, state);
if (fail) {
return fail;
}
}
return false;
}
/// Returns a FilterFunctionType that can be used in MLFunctionMatcher to
/// match a loop whose underlying load/store accesses are all varying along the
/// `fastestVaryingMemRefDimension`.
/// TODO(ntv): In the future, allow more interesting mixed layout permutation
/// once we understand better the performance implications and we are confident
/// we can build a cost model and a search procedure.
static FilterFunctionType
isVectorizableLoopPtrFactory(unsigned fastestVaryingMemRefDimension) {
return [fastestVaryingMemRefDimension](const Statement &forStmt) {
const auto &loop = cast<ForStmt>(forStmt);
return isVectorizableLoopAlongFastestVaryingMemRefDim(
loop, fastestVaryingMemRefDimension);
};
}
/// Forward-declaration.
static bool vectorizeNonRoot(MLFunctionMatches matches,
VectorizationState *state);
/// Apply vectorization of `loop` according to `state`. This is only triggered
/// if all vectorizations in `childrenMatches` have already succeeded
/// recursively in DFS post-order.
static bool doVectorize(MLFunctionMatches::EntryType oneMatch,
VectorizationState *state) {
ForStmt *loop = cast<ForStmt>(oneMatch.first);
MLFunctionMatches childrenMatches = oneMatch.second;
// 1. DFS postorder recursion, if any of my children fails, I fail too.
auto fail = vectorizeNonRoot(childrenMatches, state);
if (fail) {
// Early exit and trigger RAII cleanups at the root.
return fail;
}
// 2. This loop may have been omitted from vectorization for various reasons
// (e.g. due to the performance model or pattern depth > vector size).
auto it = state->strategy->loopToVectorDim.find(loop);
if (it == state->strategy->loopToVectorDim.end()) {
return false;
}
// 3. Actual post-order transformation.
auto vectorDim = it->second;
assert(vectorDim < state->strategy->vectorSizes.size() &&
"vector dim overflow");
// a. get actual vector size
auto vectorSize = state->strategy->vectorSizes[vectorDim];
// b. loop transformation for early vectorization is still subject to
// exploratory tradeoffs (see top of the file). Apply coarsening, i.e.:
// | ub -> ub
// | step -> step * vectorSize
LLVM_DEBUG(dbgs() << "\n[early-vect] vectorizeForStmt by " << vectorSize
<< " : ");
LLVM_DEBUG(loop->print(dbgs()));
return vectorizeForStmt(loop, loop->getStep() * vectorSize, state);
}
/// Non-root pattern iterates over the matches at this level, calls doVectorize
/// and exits early if anything below fails.
static bool vectorizeNonRoot(MLFunctionMatches matches,
VectorizationState *state) {
for (auto m : matches) {
auto fail = doVectorize(m, state);
if (fail) {
// Early exit and trigger RAII cleanups at the root.
return fail;
}
}
return false;
}
/// Tries to transform a scalar constant into a vector splat of that constant.
/// Returns the vectorized splat operation if the constant is a valid vector
/// element type.
/// If `type` is not a valid vector type or if the scalar constant is not a
/// valid vector element type, returns nullptr.
static MLValue *vectorizeConstant(Statement *stmt, const ConstantOp &constant,
Type type) {
if (!type || !type.isa<VectorType>() ||
!VectorType::isValidElementType(constant.getType())) {
return nullptr;
}
MLFuncBuilder b(stmt);
Location loc = stmt->getLoc();
auto vectorType = type.cast<VectorType>();
auto attr = SplatElementsAttr::get(vectorType, constant.getValue());
auto *constantOpStmt = cast<OperationStmt>(constant.getOperation());
OperationState state(
b.getContext(), loc, constantOpStmt->getName().getStringRef(), {},
{vectorType},
{make_pair(Identifier::get("value", b.getContext()), attr)});
auto *splat = cast<OperationStmt>(b.createOperation(state));
return cast<MLValue>(splat->getResult(0));
}
/// Returns a uniqu'ed VectorType.
/// In the case `v`'s defining statement is already part of the `state`'s
/// vectorizedSet, just returns the type of `v`.
/// Otherwise, constructs a new VectorType of shape defined by `state.strategy`
/// and of elemental type the type of `v`.
static Type getVectorType(SSAValue *v, const VectorizationState &state) {
if (!VectorType::isValidElementType(v->getType())) {
return Type();
}
auto *definingOpStmt = cast<OperationStmt>(v->getDefiningStmt());
if (state.vectorizedSet.count(definingOpStmt) > 0) {
return v->getType().cast<VectorType>();
}
return VectorType::get(state.strategy->vectorSizes, v->getType());
};
/// Tries to vectorize a given operand `op` of Statement `stmt` during def-chain
/// propagation or during terminator vectorization, by applying the following
/// logic:
/// 1. if the defining statement is part of the vectorizedSet (i.e. vectorized
/// useby -def propagation), `op` is already in the proper vector form;
/// 2. otherwise, the `op` may be in some other vector form that fails to
/// vectorize atm (i.e. broadcasting required), returns nullptr to indicate
/// failure;
/// 3. if the `op` is a constant, returns the vectorized form of the constant;
/// 4. non-constant scalars are currently non-vectorizable, in particular to
/// guard against vectorizing an index which may be loop-variant and needs
/// special handling.
///
/// In particular this logic captures some of the use cases where definitions
/// that are not scoped under the current pattern are needed to vectorize.
/// One such example is top level function constants that need to be splatted.
///
/// Returns an operand that has been vectorized to match `state`'s strategy if
/// vectorization is possible with the above logic. Returns nullptr otherwise.
///
/// TODO(ntv): handle more complex cases.
static MLValue *vectorizeOperand(SSAValue *operand, Statement *stmt,
VectorizationState *state) {
LLVM_DEBUG(dbgs() << "\n[early-vect]vectorize operand: ");
LLVM_DEBUG(operand->print(dbgs()));
auto *definingStatement = cast<OperationStmt>(operand->getDefiningStmt());
// 1. If this value has already been vectorized this round, we are done.
if (state->vectorizedSet.count(definingStatement) > 0) {
LLVM_DEBUG(dbgs() << " -> already vector operand");
return cast<MLValue>(operand);
}
// 1.b. Delayed on-demand replacement of a use.
// Note that we cannot just call replaceAllUsesWith because it may result
// in ops with mixed types, for ops whose operands have not all yet
// been vectorized. This would be invalid IR.
auto it = state->replacementMap.find(cast<MLValue>(operand));
if (it != state->replacementMap.end()) {
auto *res = cast<MLValue>(it->second);
LLVM_DEBUG(dbgs() << "-> delayed replacement by: ");
LLVM_DEBUG(res->print(dbgs()));
return res;
}
// 2. TODO(ntv): broadcast needed.
if (operand->getType().isa<VectorType>()) {
LLVM_DEBUG(dbgs() << "-> non-vectorizable");
return nullptr;
}
// 3. vectorize constant.
if (auto constant = operand->getDefiningStmt()->dyn_cast<ConstantOp>()) {
return vectorizeConstant(stmt, *constant,
getVectorType(operand, *state).cast<VectorType>());
}
// 4. currently non-vectorizable.
LLVM_DEBUG(dbgs() << "-> non-vectorizable");
LLVM_DEBUG(operand->print(dbgs()));
return nullptr;
};
/// Encodes OperationStmt-specific behavior for vectorization. In general we
/// assume that all operands of an op must be vectorized but this is not always
/// true. In the future, it would be nice to have a trait that describes how a
/// particular operation vectorizes. For now we implement the case distinction
/// here.
/// Returns a vectorized form of stmt or nullptr if vectorization fails.
/// TODO(ntv): consider adding a trait to Op to describe how it gets vectorized.
/// Maybe some Ops are not vectorizable or require some tricky logic, we cannot
/// do one-off logic here; ideally it would be TableGen'd.
static OperationStmt *vectorizeOneOperationStmt(MLFuncBuilder *b,
OperationStmt *opStmt,
VectorizationState *state) {
// Sanity checks.
assert(!opStmt->isa<LoadOp>() &&
"all loads must have already been fully vectorized independently");
assert(!opStmt->isa<VectorTransferReadOp>() &&
"vector_transfer_read cannot be further vectorized");
assert(!opStmt->isa<VectorTransferWriteOp>() &&
"vector_transfer_write cannot be further vectorized");
if (auto store = opStmt->dyn_cast<StoreOp>()) {
auto *memRef = store->getMemRef();
auto *value = store->getValueToStore();
auto *vectorValue = vectorizeOperand(value, opStmt, state);
auto indices = map(makePtrDynCaster<SSAValue>(), store->getIndices());
MLFuncBuilder b(opStmt);
auto permutationMap =
makePermutationMap(opStmt, state->strategy->loopToVectorDim);
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ permutationMap: ");
LLVM_DEBUG(permutationMap.print(dbgs()));
auto transfer = b.create<VectorTransferWriteOp>(
opStmt->getLoc(), vectorValue, memRef, indices, permutationMap);
auto *res = cast<OperationStmt>(transfer->getOperation());
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ vectorized store: " << *res);
// "Terminators" (i.e. StoreOps) are erased on the spot.
opStmt->erase();
return res;
}
auto types = map([state](SSAValue *v) { return getVectorType(v, *state); },
opStmt->getResults());
auto vectorizeOneOperand = [opStmt, state](SSAValue *op) -> SSAValue * {
return vectorizeOperand(op, opStmt, state);
};
auto operands = map(vectorizeOneOperand, opStmt->getOperands());
// Check whether a single operand is null. If so, vectorization failed.
bool success = llvm::all_of(operands, [](SSAValue *op) { return op; });
if (!success) {
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ an operand failed vectorize");
return nullptr;
}
// Create a clone of the op with the proper operands and return types.
// TODO(ntv): The following assumes there is always an op with a fixed
// name that works both in scalar mode and vector mode.
// TODO(ntv): Is it worth considering an OperationStmt.clone operation
// which changes the type so we can promote an OperationStmt with less
// boilerplate?
OperationState newOp(b->getContext(), opStmt->getLoc(),
opStmt->getName().getStringRef(), operands, types,
opStmt->getAttrs());
return b->createOperation(newOp);
}
/// Iterates over the OperationStmt in the loop and rewrites them using their
/// vectorized counterpart by:
/// 1. iteratively building a worklist of uses of the OperationStmt vectorized
/// so far by this pattern;
/// 2. for each OperationStmt in the worklist, create the vector form of this
/// operation and replace all its uses by the vectorized form. For this step,
/// the worklist must be traversed in order;
/// 3. verify that all operands of the newly vectorized operation have been
/// vectorized by this pattern.
static bool vectorizeOperations(VectorizationState *state) {
// 1. create initial worklist with the uses of the roots.
SetVector<OperationStmt *> worklist;
auto insertUsesOf = [&worklist, state](Operation *vectorized) {
for (auto *r : cast<OperationStmt>(vectorized)->getResults())
for (auto &u : r->getUses()) {
auto *stmt = cast<OperationStmt>(u.getOwner());
// Don't propagate to terminals, a separate pass is needed for those.
// TODO(ntv)[b/119759136]: use isa<> once Op is implemented.
if (state->terminators.count(stmt) > 0) {
continue;
}
worklist.insert(stmt);
}
};
apply(insertUsesOf, state->roots);
// Note: Worklist size increases iteratively. At each round we evaluate the
// size again. By construction, the order of elements in the worklist is
// consistent across iterations.
for (unsigned i = 0; i < worklist.size(); ++i) {
auto *stmt = worklist[i];
LLVM_DEBUG(dbgs() << "\n[early-vect] vectorize use: ");
LLVM_DEBUG(stmt->print(dbgs()));
// 2. Create vectorized form of the statement.
// Insert it just before stmt, on success register stmt as replaced.
MLFuncBuilder b(stmt);
auto *vectorizedStmt = vectorizeOneOperationStmt(&b, stmt, state);
if (!vectorizedStmt) {
return true;
}
// 3. Register replacement for future uses in the scop.
// Note that we cannot just call replaceAllUsesWith because it may
// result in ops with mixed types, for ops whose operands have not all
// yet been vectorized. This would be invalid IR.
state->registerReplacement(cast<OperationStmt>(stmt), vectorizedStmt);
// 4. Augment the worklist with uses of the statement we just vectorized.
// This preserves the proper order in the worklist.
apply(insertUsesOf, ArrayRef<Operation *>{stmt});
}
return false;
}
/// Vectorization is a recursive procedure where anything below can fail.
/// The root match thus needs to maintain a clone for handling failure.
/// Each root may succeed independently but will otherwise clean after itself if
/// anything below it fails.
static bool vectorizeRootMatches(MLFunctionMatches matches,
VectorizationStrategy *strategy) {
for (auto m : matches) {
auto *loop = cast<ForStmt>(m.first);
VectorizationState state;
state.strategy = strategy;
// Since patterns are recursive, they can very well intersect.
// Since we do not want a fully greedy strategy in general, we decouple
// pattern matching, from profitability analysis, from application.
// As a consequence we must check that each root pattern is still
// vectorizable. If a pattern is not vectorizable anymore, we just skip it.
// TODO(ntv): implement a non-greedy profitability analysis that keeps only
// non-intersecting patterns.
if (!isVectorizableLoop(*loop)) {
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ loop is not vectorizable");
continue;
}
MLFuncBuilder builder(loop); // builder to insert in place of loop
DenseMap<const MLValue *, MLValue *> nomap;
ForStmt *clonedLoop = cast<ForStmt>(builder.clone(*loop, nomap));
auto fail = doVectorize(m, &state);
/// Sets up error handling for this root loop. This is how the root match
/// maintains a clone for handling failure and restores the proper state via
/// RAII.
ScopeGuard sg2([&fail, loop, clonedLoop]() {
if (fail) {
loop->replaceAllUsesWith(clonedLoop);
loop->erase();
} else {
clonedLoop->erase();
}
});
if (fail) {
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ failed root doVectorize");
continue;
}
// Form the root operationsthat have been set in the replacementMap.
// For now, these roots are the loads for which vector_transfer_read
// operations have been inserted.
auto getDefiningOperation = [](const MLValue *val) {
return const_cast<MLValue *>(val)->getDefiningOperation();
};
using ReferenceTy = decltype(*(state.replacementMap.begin()));
auto getKey = [](ReferenceTy it) { return it.first; };
auto roots = map(getDefiningOperation, map(getKey, state.replacementMap));
// Vectorize the root operations and everything reached by use-def chains
// except the terminators (store statements) that need to be post-processed
// separately.
fail = vectorizeOperations(&state);
if (fail) {
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ failed vectorizeOperations");
continue;
}
// Finally, vectorize the terminators. If anything fails to vectorize, skip.
auto vectorizeOrFail = [&fail, &state](OperationStmt *stmt) {
if (fail) {
return;
}
MLFuncBuilder b(stmt);
auto *res = vectorizeOneOperationStmt(&b, stmt, &state);
if (res == nullptr) {
fail = true;
}
};
apply(vectorizeOrFail, state.terminators);
if (fail) {
LLVM_DEBUG(
dbgs() << "\n[early-vect]+++++ failed to vectorize terminators");
continue;
} else {
LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ success vectorizing pattern");
}
// Finish this vectorization pattern.
state.finishVectorizationPattern();
}
return false;
}
/// Applies vectorization to the current MLFunction by searching over a bunch of
/// predetermined patterns.
PassResult Vectorize::runOnMLFunction(MLFunction *f) {
for (auto pat : makePatterns()) {
LLVM_DEBUG(dbgs() << "\n******************************************");
LLVM_DEBUG(dbgs() << "\n******************************************");
LLVM_DEBUG(dbgs() << "\n[early-vect] new pattern on MLFunction\n");
LLVM_DEBUG(f->print(dbgs()));
unsigned patternDepth = pat.getDepth();
auto matches = pat.match(f);
// Iterate over all the top-level matches and vectorize eagerly.
// This automatically prunes intersecting matches.
for (auto m : matches) {
VectorizationStrategy strategy;
// TODO(ntv): depending on profitability, elect to reduce the vector size.
strategy.vectorSizes = clVirtualVectorSize;
auto fail = analyzeProfitability(m.second, 1, patternDepth, &strategy);
if (fail) {
continue;
}
auto *loop = cast<ForStmt>(m.first);
vectorizeLoopIfProfitable(loop, 0, patternDepth, &strategy);
// TODO(ntv): if pattern does not apply, report it; alter the
// cost/benefit.
fail = vectorizeRootMatches(matches, &strategy);
assert(!fail && "top-level failure should not happen");
// TODO(ntv): some diagnostics.
}
}
LLVM_DEBUG(dbgs() << "\n");
return PassResult::Success;
}
FunctionPass *mlir::createVectorizePass() { return new Vectorize(); }
static PassRegistration<Vectorize>
pass("vectorize",
"Vectorize to a target independent n-D vector abstraction");
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