MLIR  19.0.0git
SuperVectorize.cpp
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1 //===- SuperVectorize.cpp - Vectorize Pass Impl ---------------------------===//
2 //
3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4 // See https://llvm.org/LICENSE.txt for license information.
5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6 //
7 //===----------------------------------------------------------------------===//
8 //
9 // This file implements vectorization of loops, operations and data types to
10 // a target-independent, n-D super-vector abstraction.
11 //
12 //===----------------------------------------------------------------------===//
13 
15 
26 #include "mlir/IR/IRMapping.h"
27 #include "mlir/Pass/Pass.h"
28 #include "mlir/Support/LLVM.h"
29 #include "llvm/ADT/STLExtras.h"
30 #include "llvm/Support/Debug.h"
31 #include <optional>
32 
33 namespace mlir {
34 namespace affine {
35 #define GEN_PASS_DEF_AFFINEVECTORIZE
36 #include "mlir/Dialect/Affine/Passes.h.inc"
37 } // namespace affine
38 } // namespace mlir
39 
40 using namespace mlir;
41 using namespace affine;
42 using namespace vector;
43 
44 ///
45 /// Implements a high-level vectorization strategy on a Function.
46 /// The abstraction used is that of super-vectors, which provide a single,
47 /// compact, representation in the vector types, information that is expected
48 /// to reduce the impact of the phase ordering problem
49 ///
50 /// Vector granularity:
51 /// ===================
52 /// This pass is designed to perform vectorization at a super-vector
53 /// granularity. A super-vector is loosely defined as a vector type that is a
54 /// multiple of a "good" vector size so the HW can efficiently implement a set
55 /// of high-level primitives. Multiple is understood along any dimension; e.g.
56 /// both vector<16xf32> and vector<2x8xf32> are valid super-vectors for a
57 /// vector<8xf32> HW vector. Note that a "good vector size so the HW can
58 /// efficiently implement a set of high-level primitives" is not necessarily an
59 /// integer multiple of actual hardware registers. We leave details of this
60 /// distinction unspecified for now.
61 ///
62 /// Some may prefer the terminology a "tile of HW vectors". In this case, one
63 /// should note that super-vectors implement an "always full tile" abstraction.
64 /// They guarantee no partial-tile separation is necessary by relying on a
65 /// high-level copy-reshape abstraction that we call vector.transfer. This
66 /// copy-reshape operations is also responsible for performing layout
67 /// transposition if necessary. In the general case this will require a scoped
68 /// allocation in some notional local memory.
69 ///
70 /// Whatever the mental model one prefers to use for this abstraction, the key
71 /// point is that we burn into a single, compact, representation in the vector
72 /// types, information that is expected to reduce the impact of the phase
73 /// ordering problem. Indeed, a vector type conveys information that:
74 /// 1. the associated loops have dependency semantics that do not prevent
75 /// vectorization;
76 /// 2. the associate loops have been sliced in chunks of static sizes that are
77 /// compatible with vector sizes (i.e. similar to unroll-and-jam);
78 /// 3. the inner loops, in the unroll-and-jam analogy of 2, are captured by
79 /// the
80 /// vector type and no vectorization hampering transformations can be
81 /// applied to them anymore;
82 /// 4. the underlying memrefs are accessed in some notional contiguous way
83 /// that allows loading into vectors with some amount of spatial locality;
84 /// In other words, super-vectorization provides a level of separation of
85 /// concern by way of opacity to subsequent passes. This has the effect of
86 /// encapsulating and propagating vectorization constraints down the list of
87 /// passes until we are ready to lower further.
88 ///
89 /// For a particular target, a notion of minimal n-d vector size will be
90 /// specified and vectorization targets a multiple of those. In the following
91 /// paragraph, let "k ." represent "a multiple of", to be understood as a
92 /// multiple in the same dimension (e.g. vector<16 x k . 128> summarizes
93 /// vector<16 x 128>, vector<16 x 256>, vector<16 x 1024>, etc).
94 ///
95 /// Some non-exhaustive notable super-vector sizes of interest include:
96 /// - CPU: vector<k . HW_vector_size>,
97 /// vector<k' . core_count x k . HW_vector_size>,
98 /// vector<socket_count x k' . core_count x k . HW_vector_size>;
99 /// - GPU: vector<k . warp_size>,
100 /// vector<k . warp_size x float2>,
101 /// vector<k . warp_size x float4>,
102 /// vector<k . warp_size x 4 x 4x 4> (for tensor_core sizes).
103 ///
104 /// Loops and operations are emitted that operate on those super-vector shapes.
105 /// Subsequent lowering passes will materialize to actual HW vector sizes. These
106 /// passes are expected to be (gradually) more target-specific.
107 ///
108 /// At a high level, a vectorized load in a loop will resemble:
109 /// ```mlir
110 /// affine.for %i = ? to ? step ? {
111 /// %v_a = vector.transfer_read A[%i] : memref<?xf32>, vector<128xf32>
112 /// }
113 /// ```
114 /// It is the responsibility of the implementation of vector.transfer_read to
115 /// materialize vector registers from the original scalar memrefs. A later (more
116 /// target-dependent) lowering pass will materialize to actual HW vector sizes.
117 /// This lowering may be occur at different times:
118 /// 1. at the MLIR level into a combination of loops, unrolling, DmaStartOp +
119 /// DmaWaitOp + vectorized operations for data transformations and shuffle;
120 /// thus opening opportunities for unrolling and pipelining. This is an
121 /// instance of library call "whiteboxing"; or
122 /// 2. later in the a target-specific lowering pass or hand-written library
123 /// call; achieving full separation of concerns. This is an instance of
124 /// library call; or
125 /// 3. a mix of both, e.g. based on a model.
126 /// In the future, these operations will expose a contract to constrain the
127 /// search on vectorization patterns and sizes.
128 ///
129 /// Occurrence of super-vectorization in the compiler flow:
130 /// =======================================================
131 /// This is an active area of investigation. We start with 2 remarks to position
132 /// super-vectorization in the context of existing ongoing work: LLVM VPLAN
133 /// and LLVM SLP Vectorizer.
134 ///
135 /// LLVM VPLAN:
136 /// -----------
137 /// The astute reader may have noticed that in the limit, super-vectorization
138 /// can be applied at a similar time and with similar objectives than VPLAN.
139 /// For instance, in the case of a traditional, polyhedral compilation-flow (for
140 /// instance, the PPCG project uses ISL to provide dependence analysis,
141 /// multi-level(scheduling + tiling), lifting footprint to fast memory,
142 /// communication synthesis, mapping, register optimizations) and before
143 /// unrolling. When vectorization is applied at this *late* level in a typical
144 /// polyhedral flow, and is instantiated with actual hardware vector sizes,
145 /// super-vectorization is expected to match (or subsume) the type of patterns
146 /// that LLVM's VPLAN aims at targeting. The main difference here is that MLIR
147 /// is higher level and our implementation should be significantly simpler. Also
148 /// note that in this mode, recursive patterns are probably a bit of an overkill
149 /// although it is reasonable to expect that mixing a bit of outer loop and
150 /// inner loop vectorization + unrolling will provide interesting choices to
151 /// MLIR.
152 ///
153 /// LLVM SLP Vectorizer:
154 /// --------------------
155 /// Super-vectorization however is not meant to be usable in a similar fashion
156 /// to the SLP vectorizer. The main difference lies in the information that
157 /// both vectorizers use: super-vectorization examines contiguity of memory
158 /// references along fastest varying dimensions and loops with recursive nested
159 /// patterns capturing imperfectly-nested loop nests; the SLP vectorizer, on
160 /// the other hand, performs flat pattern matching inside a single unrolled loop
161 /// body and stitches together pieces of load and store operations into full
162 /// 1-D vectors. We envision that the SLP vectorizer is a good way to capture
163 /// innermost loop, control-flow dependent patterns that super-vectorization may
164 /// not be able to capture easily. In other words, super-vectorization does not
165 /// aim at replacing the SLP vectorizer and the two solutions are complementary.
166 ///
167 /// Ongoing investigations:
168 /// -----------------------
169 /// We discuss the following *early* places where super-vectorization is
170 /// applicable and touch on the expected benefits and risks . We list the
171 /// opportunities in the context of the traditional polyhedral compiler flow
172 /// described in PPCG. There are essentially 6 places in the MLIR pass pipeline
173 /// we expect to experiment with super-vectorization:
174 /// 1. Right after language lowering to MLIR: this is the earliest time where
175 /// super-vectorization is expected to be applied. At this level, all the
176 /// language/user/library-level annotations are available and can be fully
177 /// exploited. Examples include loop-type annotations (such as parallel,
178 /// reduction, scan, dependence distance vector, vectorizable) as well as
179 /// memory access annotations (such as non-aliasing writes guaranteed,
180 /// indirect accesses that are permutations by construction) accesses or
181 /// that a particular operation is prescribed atomic by the user. At this
182 /// level, anything that enriches what dependence analysis can do should be
183 /// aggressively exploited. At this level we are close to having explicit
184 /// vector types in the language, except we do not impose that burden on the
185 /// programmer/library: we derive information from scalar code + annotations.
186 /// 2. After dependence analysis and before polyhedral scheduling: the
187 /// information that supports vectorization does not need to be supplied by a
188 /// higher level of abstraction. Traditional dependence analysis is available
189 /// in MLIR and will be used to drive vectorization and cost models.
190 ///
191 /// Let's pause here and remark that applying super-vectorization as described
192 /// in 1. and 2. presents clear opportunities and risks:
193 /// - the opportunity is that vectorization is burned in the type system and
194 /// is protected from the adverse effect of loop scheduling, tiling, loop
195 /// interchange and all passes downstream. Provided that subsequent passes are
196 /// able to operate on vector types; the vector shapes, associated loop
197 /// iterator properties, alignment, and contiguity of fastest varying
198 /// dimensions are preserved until we lower the super-vector types. We expect
199 /// this to significantly rein in on the adverse effects of phase ordering.
200 /// - the risks are that a. all passes after super-vectorization have to work
201 /// on elemental vector types (not that this is always true, wherever
202 /// vectorization is applied) and b. that imposing vectorization constraints
203 /// too early may be overall detrimental to loop fusion, tiling and other
204 /// transformations because the dependence distances are coarsened when
205 /// operating on elemental vector types. For this reason, the pattern
206 /// profitability analysis should include a component that also captures the
207 /// maximal amount of fusion available under a particular pattern. This is
208 /// still at the stage of rough ideas but in this context, search is our
209 /// friend as the Tensor Comprehensions and auto-TVM contributions
210 /// demonstrated previously.
211 /// Bottom-line is we do not yet have good answers for the above but aim at
212 /// making it easy to answer such questions.
213 ///
214 /// Back to our listing, the last places where early super-vectorization makes
215 /// sense are:
216 /// 3. right after polyhedral-style scheduling: PLUTO-style algorithms are known
217 /// to improve locality, parallelism and be configurable (e.g. max-fuse,
218 /// smart-fuse etc). They can also have adverse effects on contiguity
219 /// properties that are required for vectorization but the vector.transfer
220 /// copy-reshape-pad-transpose abstraction is expected to help recapture
221 /// these properties.
222 /// 4. right after polyhedral-style scheduling+tiling;
223 /// 5. right after scheduling+tiling+rescheduling: points 4 and 5 represent
224 /// probably the most promising places because applying tiling achieves a
225 /// separation of concerns that allows rescheduling to worry less about
226 /// locality and more about parallelism and distribution (e.g. min-fuse).
227 ///
228 /// At these levels the risk-reward looks different: on one hand we probably
229 /// lost a good deal of language/user/library-level annotation; on the other
230 /// hand we gained parallelism and locality through scheduling and tiling.
231 /// However we probably want to ensure tiling is compatible with the
232 /// full-tile-only abstraction used in super-vectorization or suffer the
233 /// consequences. It is too early to place bets on what will win but we expect
234 /// super-vectorization to be the right abstraction to allow exploring at all
235 /// these levels. And again, search is our friend.
236 ///
237 /// Lastly, we mention it again here:
238 /// 6. as a MLIR-based alternative to VPLAN.
239 ///
240 /// Lowering, unrolling, pipelining:
241 /// ================================
242 /// TODO: point to the proper places.
243 ///
244 /// Algorithm:
245 /// ==========
246 /// The algorithm proceeds in a few steps:
247 /// 1. defining super-vectorization patterns and matching them on the tree of
248 /// AffineForOp. A super-vectorization pattern is defined as a recursive
249 /// data structures that matches and captures nested, imperfectly-nested
250 /// loops that have a. conformable loop annotations attached (e.g. parallel,
251 /// reduction, vectorizable, ...) as well as b. all contiguous load/store
252 /// operations along a specified minor dimension (not necessarily the
253 /// fastest varying) ;
254 /// 2. analyzing those patterns for profitability (TODO: and
255 /// interference);
256 /// 3. then, for each pattern in order:
257 /// a. applying iterative rewriting of the loops and all their nested
258 /// operations in topological order. Rewriting is implemented by
259 /// coarsening the loops and converting operations and operands to their
260 /// vector forms. Processing operations in topological order is relatively
261 /// simple due to the structured nature of the control-flow
262 /// representation. This order ensures that all the operands of a given
263 /// operation have been vectorized before the operation itself in a single
264 /// traversal, except for operands defined outside of the loop nest. The
265 /// algorithm can convert the following operations to their vector form:
266 /// * Affine load and store operations are converted to opaque vector
267 /// transfer read and write operations.
268 /// * Scalar constant operations/operands are converted to vector
269 /// constant operations (splat).
270 /// * Uniform operands (only induction variables of loops not mapped to
271 /// a vector dimension, or operands defined outside of the loop nest
272 /// for now) are broadcasted to a vector.
273 /// TODO: Support more uniform cases.
274 /// * Affine for operations with 'iter_args' are vectorized by
275 /// vectorizing their 'iter_args' operands and results.
276 /// TODO: Support more complex loops with divergent lbs and/or ubs.
277 /// * The remaining operations in the loop nest are vectorized by
278 /// widening their scalar types to vector types.
279 /// b. if everything under the root AffineForOp in the current pattern
280 /// is vectorized properly, we commit that loop to the IR and remove the
281 /// scalar loop. Otherwise, we discard the vectorized loop and keep the
282 /// original scalar loop.
283 /// c. vectorization is applied on the next pattern in the list. Because
284 /// pattern interference avoidance is not yet implemented and that we do
285 /// not support further vectorizing an already vector load we need to
286 /// re-verify that the pattern is still vectorizable. This is expected to
287 /// make cost models more difficult to write and is subject to improvement
288 /// in the future.
289 ///
290 /// Choice of loop transformation to support the algorithm:
291 /// =======================================================
292 /// The choice of loop transformation to apply for coarsening vectorized loops
293 /// is still subject to exploratory tradeoffs. In particular, say we want to
294 /// vectorize by a factor 128, we want to transform the following input:
295 /// ```mlir
296 /// affine.for %i = %M to %N {
297 /// %a = affine.load %A[%i] : memref<?xf32>
298 /// }
299 /// ```
300 ///
301 /// Traditionally, one would vectorize late (after scheduling, tiling,
302 /// memory promotion etc) say after stripmining (and potentially unrolling in
303 /// the case of LLVM's SLP vectorizer):
304 /// ```mlir
305 /// affine.for %i = floor(%M, 128) to ceil(%N, 128) {
306 /// affine.for %ii = max(%M, 128 * %i) to min(%N, 128*%i + 127) {
307 /// %a = affine.load %A[%ii] : memref<?xf32>
308 /// }
309 /// }
310 /// ```
311 ///
312 /// Instead, we seek to vectorize early and freeze vector types before
313 /// scheduling, so we want to generate a pattern that resembles:
314 /// ```mlir
315 /// affine.for %i = ? to ? step ? {
316 /// %v_a = vector.transfer_read %A[%i] : memref<?xf32>, vector<128xf32>
317 /// }
318 /// ```
319 ///
320 /// i. simply dividing the lower / upper bounds by 128 creates issues
321 /// when representing expressions such as ii + 1 because now we only
322 /// have access to original values that have been divided. Additional
323 /// information is needed to specify accesses at below-128 granularity;
324 /// ii. another alternative is to coarsen the loop step but this may have
325 /// consequences on dependence analysis and fusability of loops: fusable
326 /// loops probably need to have the same step (because we don't want to
327 /// stripmine/unroll to enable fusion).
328 /// As a consequence, we choose to represent the coarsening using the loop
329 /// step for now and reevaluate in the future. Note that we can renormalize
330 /// loop steps later if/when we have evidence that they are problematic.
331 ///
332 /// For the simple strawman example above, vectorizing for a 1-D vector
333 /// abstraction of size 128 returns code similar to:
334 /// ```mlir
335 /// affine.for %i = %M to %N step 128 {
336 /// %v_a = vector.transfer_read %A[%i] : memref<?xf32>, vector<128xf32>
337 /// }
338 /// ```
339 ///
340 /// Unsupported cases, extensions, and work in progress (help welcome :-) ):
341 /// ========================================================================
342 /// 1. lowering to concrete vector types for various HW;
343 /// 2. reduction support for n-D vectorization and non-unit steps;
344 /// 3. non-effecting padding during vector.transfer_read and filter during
345 /// vector.transfer_write;
346 /// 4. misalignment support vector.transfer_read / vector.transfer_write
347 /// (hopefully without read-modify-writes);
348 /// 5. control-flow support;
349 /// 6. cost-models, heuristics and search;
350 /// 7. Op implementation, extensions and implication on memref views;
351 /// 8. many TODOs left around.
352 ///
353 /// Examples:
354 /// =========
355 /// Consider the following Function:
356 /// ```mlir
357 /// func @vector_add_2d(%M : index, %N : index) -> f32 {
358 /// %A = alloc (%M, %N) : memref<?x?xf32, 0>
359 /// %B = alloc (%M, %N) : memref<?x?xf32, 0>
360 /// %C = alloc (%M, %N) : memref<?x?xf32, 0>
361 /// %f1 = arith.constant 1.0 : f32
362 /// %f2 = arith.constant 2.0 : f32
363 /// affine.for %i0 = 0 to %M {
364 /// affine.for %i1 = 0 to %N {
365 /// // non-scoped %f1
366 /// affine.store %f1, %A[%i0, %i1] : memref<?x?xf32, 0>
367 /// }
368 /// }
369 /// affine.for %i2 = 0 to %M {
370 /// affine.for %i3 = 0 to %N {
371 /// // non-scoped %f2
372 /// affine.store %f2, %B[%i2, %i3] : memref<?x?xf32, 0>
373 /// }
374 /// }
375 /// affine.for %i4 = 0 to %M {
376 /// affine.for %i5 = 0 to %N {
377 /// %a5 = affine.load %A[%i4, %i5] : memref<?x?xf32, 0>
378 /// %b5 = affine.load %B[%i4, %i5] : memref<?x?xf32, 0>
379 /// %s5 = arith.addf %a5, %b5 : f32
380 /// // non-scoped %f1
381 /// %s6 = arith.addf %s5, %f1 : f32
382 /// // non-scoped %f2
383 /// %s7 = arith.addf %s5, %f2 : f32
384 /// // diamond dependency.
385 /// %s8 = arith.addf %s7, %s6 : f32
386 /// affine.store %s8, %C[%i4, %i5] : memref<?x?xf32, 0>
387 /// }
388 /// }
389 /// %c7 = arith.constant 7 : index
390 /// %c42 = arith.constant 42 : index
391 /// %res = load %C[%c7, %c42] : memref<?x?xf32, 0>
392 /// return %res : f32
393 /// }
394 /// ```
395 ///
396 /// The -affine-super-vectorize pass with the following arguments:
397 /// ```
398 /// -affine-super-vectorize="virtual-vector-size=256 test-fastest-varying=0"
399 /// ```
400 ///
401 /// produces this standard innermost-loop vectorized code:
402 /// ```mlir
403 /// func @vector_add_2d(%arg0 : index, %arg1 : index) -> f32 {
404 /// %0 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
405 /// %1 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
406 /// %2 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
407 /// %cst = arith.constant 1.0 : f32
408 /// %cst_0 = arith.constant 2.0 : f32
409 /// affine.for %i0 = 0 to %arg0 {
410 /// affine.for %i1 = 0 to %arg1 step 256 {
411 /// %cst_1 = arith.constant dense<vector<256xf32>, 1.0> :
412 /// vector<256xf32>
413 /// vector.transfer_write %cst_1, %0[%i0, %i1] :
414 /// vector<256xf32>, memref<?x?xf32>
415 /// }
416 /// }
417 /// affine.for %i2 = 0 to %arg0 {
418 /// affine.for %i3 = 0 to %arg1 step 256 {
419 /// %cst_2 = arith.constant dense<vector<256xf32>, 2.0> :
420 /// vector<256xf32>
421 /// vector.transfer_write %cst_2, %1[%i2, %i3] :
422 /// vector<256xf32>, memref<?x?xf32>
423 /// }
424 /// }
425 /// affine.for %i4 = 0 to %arg0 {
426 /// affine.for %i5 = 0 to %arg1 step 256 {
427 /// %3 = vector.transfer_read %0[%i4, %i5] :
428 /// memref<?x?xf32>, vector<256xf32>
429 /// %4 = vector.transfer_read %1[%i4, %i5] :
430 /// memref<?x?xf32>, vector<256xf32>
431 /// %5 = arith.addf %3, %4 : vector<256xf32>
432 /// %cst_3 = arith.constant dense<vector<256xf32>, 1.0> :
433 /// vector<256xf32>
434 /// %6 = arith.addf %5, %cst_3 : vector<256xf32>
435 /// %cst_4 = arith.constant dense<vector<256xf32>, 2.0> :
436 /// vector<256xf32>
437 /// %7 = arith.addf %5, %cst_4 : vector<256xf32>
438 /// %8 = arith.addf %7, %6 : vector<256xf32>
439 /// vector.transfer_write %8, %2[%i4, %i5] :
440 /// vector<256xf32>, memref<?x?xf32>
441 /// }
442 /// }
443 /// %c7 = arith.constant 7 : index
444 /// %c42 = arith.constant 42 : index
445 /// %9 = load %2[%c7, %c42] : memref<?x?xf32>
446 /// return %9 : f32
447 /// }
448 /// ```
449 ///
450 /// The -affine-super-vectorize pass with the following arguments:
451 /// ```
452 /// -affine-super-vectorize="virtual-vector-size=32,256 \
453 /// test-fastest-varying=1,0"
454 /// ```
455 ///
456 /// produces this more interesting mixed outer-innermost-loop vectorized code:
457 /// ```mlir
458 /// func @vector_add_2d(%arg0 : index, %arg1 : index) -> f32 {
459 /// %0 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
460 /// %1 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
461 /// %2 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
462 /// %cst = arith.constant 1.0 : f32
463 /// %cst_0 = arith.constant 2.0 : f32
464 /// affine.for %i0 = 0 to %arg0 step 32 {
465 /// affine.for %i1 = 0 to %arg1 step 256 {
466 /// %cst_1 = arith.constant dense<vector<32x256xf32>, 1.0> :
467 /// vector<32x256xf32>
468 /// vector.transfer_write %cst_1, %0[%i0, %i1] :
469 /// vector<32x256xf32>, memref<?x?xf32>
470 /// }
471 /// }
472 /// affine.for %i2 = 0 to %arg0 step 32 {
473 /// affine.for %i3 = 0 to %arg1 step 256 {
474 /// %cst_2 = arith.constant dense<vector<32x256xf32>, 2.0> :
475 /// vector<32x256xf32>
476 /// vector.transfer_write %cst_2, %1[%i2, %i3] :
477 /// vector<32x256xf32>, memref<?x?xf32>
478 /// }
479 /// }
480 /// affine.for %i4 = 0 to %arg0 step 32 {
481 /// affine.for %i5 = 0 to %arg1 step 256 {
482 /// %3 = vector.transfer_read %0[%i4, %i5] :
483 /// memref<?x?xf32> vector<32x256xf32>
484 /// %4 = vector.transfer_read %1[%i4, %i5] :
485 /// memref<?x?xf32>, vector<32x256xf32>
486 /// %5 = arith.addf %3, %4 : vector<32x256xf32>
487 /// %cst_3 = arith.constant dense<vector<32x256xf32>, 1.0> :
488 /// vector<32x256xf32>
489 /// %6 = arith.addf %5, %cst_3 : vector<32x256xf32>
490 /// %cst_4 = arith.constant dense<vector<32x256xf32>, 2.0> :
491 /// vector<32x256xf32>
492 /// %7 = arith.addf %5, %cst_4 : vector<32x256xf32>
493 /// %8 = arith.addf %7, %6 : vector<32x256xf32>
494 /// vector.transfer_write %8, %2[%i4, %i5] :
495 /// vector<32x256xf32>, memref<?x?xf32>
496 /// }
497 /// }
498 /// %c7 = arith.constant 7 : index
499 /// %c42 = arith.constant 42 : index
500 /// %9 = load %2[%c7, %c42] : memref<?x?xf32>
501 /// return %9 : f32
502 /// }
503 /// ```
504 ///
505 /// Of course, much more intricate n-D imperfectly-nested patterns can be
506 /// vectorized too and specified in a fully declarative fashion.
507 ///
508 /// Reduction:
509 /// ==========
510 /// Vectorizing reduction loops along the reduction dimension is supported if:
511 /// - the reduction kind is supported,
512 /// - the vectorization is 1-D, and
513 /// - the step size of the loop equals to one.
514 ///
515 /// Comparing to the non-vector-dimension case, two additional things are done
516 /// during vectorization of such loops:
517 /// - The resulting vector returned from the loop is reduced to a scalar using
518 /// `vector.reduce`.
519 /// - In some cases a mask is applied to the vector yielded at the end of the
520 /// loop to prevent garbage values from being written to the accumulator.
521 ///
522 /// Reduction vectorization is switched off by default, it can be enabled by
523 /// passing a map from loops to reductions to utility functions, or by passing
524 /// `vectorize-reductions=true` to the vectorization pass.
525 ///
526 /// Consider the following example:
527 /// ```mlir
528 /// func @vecred(%in: memref<512xf32>) -> f32 {
529 /// %cst = arith.constant 0.000000e+00 : f32
530 /// %sum = affine.for %i = 0 to 500 iter_args(%part_sum = %cst) -> (f32) {
531 /// %ld = affine.load %in[%i] : memref<512xf32>
532 /// %cos = math.cos %ld : f32
533 /// %add = arith.addf %part_sum, %cos : f32
534 /// affine.yield %add : f32
535 /// }
536 /// return %sum : f32
537 /// }
538 /// ```
539 ///
540 /// The -affine-super-vectorize pass with the following arguments:
541 /// ```
542 /// -affine-super-vectorize="virtual-vector-size=128 test-fastest-varying=0 \
543 /// vectorize-reductions=true"
544 /// ```
545 /// produces the following output:
546 /// ```mlir
547 /// #map = affine_map<(d0) -> (-d0 + 500)>
548 /// func @vecred(%arg0: memref<512xf32>) -> f32 {
549 /// %cst = arith.constant 0.000000e+00 : f32
550 /// %cst_0 = arith.constant dense<0.000000e+00> : vector<128xf32>
551 /// %0 = affine.for %arg1 = 0 to 500 step 128 iter_args(%arg2 = %cst_0)
552 /// -> (vector<128xf32>) {
553 /// // %2 is the number of iterations left in the original loop.
554 /// %2 = affine.apply #map(%arg1)
555 /// %3 = vector.create_mask %2 : vector<128xi1>
556 /// %cst_1 = arith.constant 0.000000e+00 : f32
557 /// %4 = vector.transfer_read %arg0[%arg1], %cst_1 :
558 /// memref<512xf32>, vector<128xf32>
559 /// %5 = math.cos %4 : vector<128xf32>
560 /// %6 = arith.addf %arg2, %5 : vector<128xf32>
561 /// // We filter out the effect of last 12 elements using the mask.
562 /// %7 = select %3, %6, %arg2 : vector<128xi1>, vector<128xf32>
563 /// affine.yield %7 : vector<128xf32>
564 /// }
565 /// %1 = vector.reduction <add>, %0 : vector<128xf32> into f32
566 /// return %1 : f32
567 /// }
568 /// ```
569 ///
570 /// Note that because of loop misalignment we needed to apply a mask to prevent
571 /// last 12 elements from affecting the final result. The mask is full of ones
572 /// in every iteration except for the last one, in which it has the form
573 /// `11...100...0` with 116 ones and 12 zeros.
574 
575 #define DEBUG_TYPE "early-vect"
576 
577 using llvm::dbgs;
578 
579 /// Forward declaration.
580 static FilterFunctionType
582  int fastestVaryingMemRefDimension);
583 
584 /// Creates a vectorization pattern from the command line arguments.
585 /// Up to 3-D patterns are supported.
586 /// If the command line argument requests a pattern of higher order, returns an
587 /// empty pattern list which will conservatively result in no vectorization.
588 static std::optional<NestedPattern>
589 makePattern(const DenseSet<Operation *> &parallelLoops, int vectorRank,
590  ArrayRef<int64_t> fastestVaryingPattern) {
591  using affine::matcher::For;
592  int64_t d0 = fastestVaryingPattern.empty() ? -1 : fastestVaryingPattern[0];
593  int64_t d1 = fastestVaryingPattern.size() < 2 ? -1 : fastestVaryingPattern[1];
594  int64_t d2 = fastestVaryingPattern.size() < 3 ? -1 : fastestVaryingPattern[2];
595  switch (vectorRank) {
596  case 1:
597  return For(isVectorizableLoopPtrFactory(parallelLoops, d0));
598  case 2:
599  return For(isVectorizableLoopPtrFactory(parallelLoops, d0),
600  For(isVectorizableLoopPtrFactory(parallelLoops, d1)));
601  case 3:
602  return For(isVectorizableLoopPtrFactory(parallelLoops, d0),
603  For(isVectorizableLoopPtrFactory(parallelLoops, d1),
604  For(isVectorizableLoopPtrFactory(parallelLoops, d2))));
605  default: {
606  return std::nullopt;
607  }
608  }
609 }
610 
612  static auto pattern = affine::matcher::Op(
613  llvm::IsaPred<vector::TransferReadOp, vector::TransferWriteOp>);
614  return pattern;
615 }
616 
617 namespace {
618 
619 /// Base state for the vectorize pass.
620 /// Command line arguments are preempted by non-empty pass arguments.
621 struct Vectorize : public affine::impl::AffineVectorizeBase<Vectorize> {
622  using Base::Base;
623 
624  void runOnOperation() override;
625 };
626 
627 } // namespace
628 
629 static void vectorizeLoopIfProfitable(Operation *loop, unsigned depthInPattern,
630  unsigned patternDepth,
631  VectorizationStrategy *strategy) {
632  assert(patternDepth > depthInPattern &&
633  "patternDepth is greater than depthInPattern");
634  if (patternDepth - depthInPattern > strategy->vectorSizes.size()) {
635  // Don't vectorize this loop
636  return;
637  }
638  strategy->loopToVectorDim[loop] =
639  strategy->vectorSizes.size() - (patternDepth - depthInPattern);
640 }
641 
642 /// Implements a simple strawman strategy for vectorization.
643 /// Given a matched pattern `matches` of depth `patternDepth`, this strategy
644 /// greedily assigns the fastest varying dimension ** of the vector ** to the
645 /// innermost loop in the pattern.
646 /// When coupled with a pattern that looks for the fastest varying dimension in
647 /// load/store MemRefs, this creates a generic vectorization strategy that works
648 /// for any loop in a hierarchy (outermost, innermost or intermediate).
649 ///
650 /// TODO: In the future we should additionally increase the power of the
651 /// profitability analysis along 3 directions:
652 /// 1. account for loop extents (both static and parametric + annotations);
653 /// 2. account for data layout permutations;
654 /// 3. account for impact of vectorization on maximal loop fusion.
655 /// Then we can quantify the above to build a cost model and search over
656 /// strategies.
658  unsigned depthInPattern,
659  unsigned patternDepth,
660  VectorizationStrategy *strategy) {
661  for (auto m : matches) {
662  if (failed(analyzeProfitability(m.getMatchedChildren(), depthInPattern + 1,
663  patternDepth, strategy))) {
664  return failure();
665  }
666  vectorizeLoopIfProfitable(m.getMatchedOperation(), depthInPattern,
667  patternDepth, strategy);
668  }
669  return success();
670 }
671 
672 ///// end TODO: Hoist to a VectorizationStrategy.cpp when appropriate /////
673 
674 namespace {
675 
676 struct VectorizationState {
677 
678  VectorizationState(MLIRContext *context) : builder(context) {}
679 
680  /// Registers the vector replacement of a scalar operation and its result
681  /// values. Both operations must have the same number of results.
682  ///
683  /// This utility is used to register the replacement for the vast majority of
684  /// the vectorized operations.
685  ///
686  /// Example:
687  /// * 'replaced': %0 = arith.addf %1, %2 : f32
688  /// * 'replacement': %0 = arith.addf %1, %2 : vector<128xf32>
689  void registerOpVectorReplacement(Operation *replaced, Operation *replacement);
690 
691  /// Registers the vector replacement of a scalar value. The replacement
692  /// operation should have a single result, which replaces the scalar value.
693  ///
694  /// This utility is used to register the vector replacement of block arguments
695  /// and operation results which are not directly vectorized (i.e., their
696  /// scalar version still exists after vectorization), like uniforms.
697  ///
698  /// Example:
699  /// * 'replaced': block argument or operation outside of the vectorized
700  /// loop.
701  /// * 'replacement': %0 = vector.broadcast %1 : f32 to vector<128xf32>
702  void registerValueVectorReplacement(Value replaced, Operation *replacement);
703 
704  /// Registers the vector replacement of a block argument (e.g., iter_args).
705  ///
706  /// Example:
707  /// * 'replaced': 'iter_arg' block argument.
708  /// * 'replacement': vectorized 'iter_arg' block argument.
709  void registerBlockArgVectorReplacement(BlockArgument replaced,
710  BlockArgument replacement);
711 
712  /// Registers the scalar replacement of a scalar value. 'replacement' must be
713  /// scalar.
714  ///
715  /// This utility is used to register the replacement of block arguments
716  /// or affine.apply results that are within the loop be vectorized and will
717  /// continue being scalar within the vector loop.
718  ///
719  /// Example:
720  /// * 'replaced': induction variable of a loop to be vectorized.
721  /// * 'replacement': new induction variable in the new vector loop.
722  void registerValueScalarReplacement(Value replaced, Value replacement);
723 
724  /// Registers the scalar replacement of a scalar result returned from a
725  /// reduction loop. 'replacement' must be scalar.
726  ///
727  /// This utility is used to register the replacement for scalar results of
728  /// vectorized reduction loops with iter_args.
729  ///
730  /// Example 2:
731  /// * 'replaced': %0 = affine.for %i = 0 to 512 iter_args(%x = ...) -> (f32)
732  /// * 'replacement': %1 = vector.reduction <add>, %0 : vector<4xf32> into
733  /// f32
734  void registerLoopResultScalarReplacement(Value replaced, Value replacement);
735 
736  /// Returns in 'replacedVals' the scalar replacement for values in
737  /// 'inputVals'.
738  void getScalarValueReplacementsFor(ValueRange inputVals,
739  SmallVectorImpl<Value> &replacedVals);
740 
741  /// Erases the scalar loop nest after its successful vectorization.
742  void finishVectorizationPattern(AffineForOp rootLoop);
743 
744  // Used to build and insert all the new operations created. The insertion
745  // point is preserved and updated along the vectorization process.
746  OpBuilder builder;
747 
748  // Maps input scalar operations to their vector counterparts.
749  DenseMap<Operation *, Operation *> opVectorReplacement;
750  // Maps input scalar values to their vector counterparts.
751  IRMapping valueVectorReplacement;
752  // Maps input scalar values to their new scalar counterparts in the vector
753  // loop nest.
754  IRMapping valueScalarReplacement;
755  // Maps results of reduction loops to their new scalar counterparts.
756  DenseMap<Value, Value> loopResultScalarReplacement;
757 
758  // Maps the newly created vector loops to their vector dimension.
759  DenseMap<Operation *, unsigned> vecLoopToVecDim;
760 
761  // Maps the new vectorized loops to the corresponding vector masks if it is
762  // required.
763  DenseMap<Operation *, Value> vecLoopToMask;
764 
765  // The strategy drives which loop to vectorize by which amount.
766  const VectorizationStrategy *strategy = nullptr;
767 
768 private:
769  /// Internal implementation to map input scalar values to new vector or scalar
770  /// values.
771  void registerValueVectorReplacementImpl(Value replaced, Value replacement);
772 };
773 
774 } // namespace
775 
776 /// Registers the vector replacement of a scalar operation and its result
777 /// values. Both operations must have the same number of results.
778 ///
779 /// This utility is used to register the replacement for the vast majority of
780 /// the vectorized operations.
781 ///
782 /// Example:
783 /// * 'replaced': %0 = arith.addf %1, %2 : f32
784 /// * 'replacement': %0 = arith.addf %1, %2 : vector<128xf32>
785 void VectorizationState::registerOpVectorReplacement(Operation *replaced,
786  Operation *replacement) {
787  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ commit vectorized op:\n");
788  LLVM_DEBUG(dbgs() << *replaced << "\n");
789  LLVM_DEBUG(dbgs() << "into\n");
790  LLVM_DEBUG(dbgs() << *replacement << "\n");
791 
792  assert(replaced->getNumResults() == replacement->getNumResults() &&
793  "Unexpected replaced and replacement results");
794  assert(opVectorReplacement.count(replaced) == 0 && "already registered");
795  opVectorReplacement[replaced] = replacement;
796 
797  for (auto resultTuple :
798  llvm::zip(replaced->getResults(), replacement->getResults()))
799  registerValueVectorReplacementImpl(std::get<0>(resultTuple),
800  std::get<1>(resultTuple));
801 }
802 
803 /// Registers the vector replacement of a scalar value. The replacement
804 /// operation should have a single result, which replaces the scalar value.
805 ///
806 /// This utility is used to register the vector replacement of block arguments
807 /// and operation results which are not directly vectorized (i.e., their
808 /// scalar version still exists after vectorization), like uniforms.
809 ///
810 /// Example:
811 /// * 'replaced': block argument or operation outside of the vectorized loop.
812 /// * 'replacement': %0 = vector.broadcast %1 : f32 to vector<128xf32>
813 void VectorizationState::registerValueVectorReplacement(
814  Value replaced, Operation *replacement) {
815  assert(replacement->getNumResults() == 1 &&
816  "Expected single-result replacement");
817  if (Operation *defOp = replaced.getDefiningOp())
818  registerOpVectorReplacement(defOp, replacement);
819  else
820  registerValueVectorReplacementImpl(replaced, replacement->getResult(0));
821 }
822 
823 /// Registers the vector replacement of a block argument (e.g., iter_args).
824 ///
825 /// Example:
826 /// * 'replaced': 'iter_arg' block argument.
827 /// * 'replacement': vectorized 'iter_arg' block argument.
828 void VectorizationState::registerBlockArgVectorReplacement(
829  BlockArgument replaced, BlockArgument replacement) {
830  registerValueVectorReplacementImpl(replaced, replacement);
831 }
832 
833 void VectorizationState::registerValueVectorReplacementImpl(Value replaced,
834  Value replacement) {
835  assert(!valueVectorReplacement.contains(replaced) &&
836  "Vector replacement already registered");
837  assert(isa<VectorType>(replacement.getType()) &&
838  "Expected vector type in vector replacement");
839  valueVectorReplacement.map(replaced, replacement);
840 }
841 
842 /// Registers the scalar replacement of a scalar value. 'replacement' must be
843 /// scalar.
844 ///
845 /// This utility is used to register the replacement of block arguments
846 /// or affine.apply results that are within the loop be vectorized and will
847 /// continue being scalar within the vector loop.
848 ///
849 /// Example:
850 /// * 'replaced': induction variable of a loop to be vectorized.
851 /// * 'replacement': new induction variable in the new vector loop.
852 void VectorizationState::registerValueScalarReplacement(Value replaced,
853  Value replacement) {
854  assert(!valueScalarReplacement.contains(replaced) &&
855  "Scalar value replacement already registered");
856  assert(!isa<VectorType>(replacement.getType()) &&
857  "Expected scalar type in scalar replacement");
858  valueScalarReplacement.map(replaced, replacement);
859 }
860 
861 /// Registers the scalar replacement of a scalar result returned from a
862 /// reduction loop. 'replacement' must be scalar.
863 ///
864 /// This utility is used to register the replacement for scalar results of
865 /// vectorized reduction loops with iter_args.
866 ///
867 /// Example 2:
868 /// * 'replaced': %0 = affine.for %i = 0 to 512 iter_args(%x = ...) -> (f32)
869 /// * 'replacement': %1 = vector.reduction <add>, %0 : vector<4xf32> into f32
870 void VectorizationState::registerLoopResultScalarReplacement(
871  Value replaced, Value replacement) {
872  assert(isa<AffineForOp>(replaced.getDefiningOp()));
873  assert(loopResultScalarReplacement.count(replaced) == 0 &&
874  "already registered");
875  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ will replace a result of the loop "
876  "with scalar: "
877  << replacement);
878  loopResultScalarReplacement[replaced] = replacement;
879 }
880 
881 /// Returns in 'replacedVals' the scalar replacement for values in 'inputVals'.
882 void VectorizationState::getScalarValueReplacementsFor(
883  ValueRange inputVals, SmallVectorImpl<Value> &replacedVals) {
884  for (Value inputVal : inputVals)
885  replacedVals.push_back(valueScalarReplacement.lookupOrDefault(inputVal));
886 }
887 
888 /// Erases a loop nest, including all its nested operations.
889 static void eraseLoopNest(AffineForOp forOp) {
890  LLVM_DEBUG(dbgs() << "[early-vect]+++++ erasing:\n" << forOp << "\n");
891  forOp.erase();
892 }
893 
894 /// Erases the scalar loop nest after its successful vectorization.
895 void VectorizationState::finishVectorizationPattern(AffineForOp rootLoop) {
896  LLVM_DEBUG(dbgs() << "\n[early-vect] Finalizing vectorization\n");
897  eraseLoopNest(rootLoop);
898 }
899 
900 // Apply 'map' with 'mapOperands' returning resulting values in 'results'.
902  ValueRange mapOperands,
903  VectorizationState &state,
904  SmallVectorImpl<Value> &results) {
905  for (auto resultExpr : map.getResults()) {
906  auto singleResMap =
907  AffineMap::get(map.getNumDims(), map.getNumSymbols(), resultExpr);
908  auto afOp = state.builder.create<AffineApplyOp>(op->getLoc(), singleResMap,
909  mapOperands);
910  results.push_back(afOp);
911  }
912 }
913 
914 /// Returns a FilterFunctionType that can be used in NestedPattern to match a
915 /// loop whose underlying load/store accesses are either invariant or all
916 // varying along the `fastestVaryingMemRefDimension`.
917 static FilterFunctionType
919  int fastestVaryingMemRefDimension) {
920  return [&parallelLoops, fastestVaryingMemRefDimension](Operation &forOp) {
921  auto loop = cast<AffineForOp>(forOp);
922  if (!parallelLoops.contains(loop))
923  return false;
924  int memRefDim = -1;
925  auto vectorizableBody =
926  isVectorizableLoopBody(loop, &memRefDim, vectorTransferPattern());
927  if (!vectorizableBody)
928  return false;
929  return memRefDim == -1 || fastestVaryingMemRefDimension == -1 ||
930  memRefDim == fastestVaryingMemRefDimension;
931  };
932 }
933 
934 /// Returns the vector type resulting from applying the provided vectorization
935 /// strategy on the scalar type.
936 static VectorType getVectorType(Type scalarTy,
937  const VectorizationStrategy *strategy) {
938  assert(!isa<VectorType>(scalarTy) && "Expected scalar type");
939  return VectorType::get(strategy->vectorSizes, scalarTy);
940 }
941 
942 /// Tries to transform a scalar constant into a vector constant. Returns the
943 /// vector constant if the scalar type is valid vector element type. Returns
944 /// nullptr, otherwise.
945 static arith::ConstantOp vectorizeConstant(arith::ConstantOp constOp,
946  VectorizationState &state) {
947  Type scalarTy = constOp.getType();
948  if (!VectorType::isValidElementType(scalarTy))
949  return nullptr;
950 
951  auto vecTy = getVectorType(scalarTy, state.strategy);
952  auto vecAttr = DenseElementsAttr::get(vecTy, constOp.getValue());
953 
954  OpBuilder::InsertionGuard guard(state.builder);
955  Operation *parentOp = state.builder.getInsertionBlock()->getParentOp();
956  // Find the innermost vectorized ancestor loop to insert the vector constant.
957  while (parentOp && !state.vecLoopToVecDim.count(parentOp))
958  parentOp = parentOp->getParentOp();
959  assert(parentOp && state.vecLoopToVecDim.count(parentOp) &&
960  isa<AffineForOp>(parentOp) && "Expected a vectorized for op");
961  auto vecForOp = cast<AffineForOp>(parentOp);
962  state.builder.setInsertionPointToStart(vecForOp.getBody());
963  auto newConstOp =
964  state.builder.create<arith::ConstantOp>(constOp.getLoc(), vecAttr);
965 
966  // Register vector replacement for future uses in the scope.
967  state.registerOpVectorReplacement(constOp, newConstOp);
968  return newConstOp;
969 }
970 
971 /// We have no need to vectorize affine.apply. However, we still need to
972 /// generate it and replace the operands with values in valueScalarReplacement.
973 static Operation *vectorizeAffineApplyOp(AffineApplyOp applyOp,
974  VectorizationState &state) {
975  SmallVector<Value, 8> updatedOperands;
976  for (Value operand : applyOp.getOperands()) {
977  if (state.valueVectorReplacement.contains(operand)) {
978  LLVM_DEBUG(
979  dbgs() << "\n[early-vect]+++++ affine.apply on vector operand\n");
980  return nullptr;
981  } else {
982  Value updatedOperand = state.valueScalarReplacement.lookupOrNull(operand);
983  if (!updatedOperand)
984  updatedOperand = operand;
985  updatedOperands.push_back(updatedOperand);
986  }
987  }
988 
989  auto newApplyOp = state.builder.create<AffineApplyOp>(
990  applyOp.getLoc(), applyOp.getAffineMap(), updatedOperands);
991 
992  // Register the new affine.apply result.
993  state.registerValueScalarReplacement(applyOp.getResult(),
994  newApplyOp.getResult());
995  return newApplyOp;
996 }
997 
998 /// Creates a constant vector filled with the neutral elements of the given
999 /// reduction. The scalar type of vector elements will be taken from
1000 /// `oldOperand`.
1001 static arith::ConstantOp createInitialVector(arith::AtomicRMWKind reductionKind,
1002  Value oldOperand,
1003  VectorizationState &state) {
1004  Type scalarTy = oldOperand.getType();
1005  if (!VectorType::isValidElementType(scalarTy))
1006  return nullptr;
1007 
1008  Attribute valueAttr = getIdentityValueAttr(
1009  reductionKind, scalarTy, state.builder, oldOperand.getLoc());
1010  auto vecTy = getVectorType(scalarTy, state.strategy);
1011  auto vecAttr = DenseElementsAttr::get(vecTy, valueAttr);
1012  auto newConstOp =
1013  state.builder.create<arith::ConstantOp>(oldOperand.getLoc(), vecAttr);
1014 
1015  return newConstOp;
1016 }
1017 
1018 /// Creates a mask used to filter out garbage elements in the last iteration
1019 /// of unaligned loops. If a mask is not required then `nullptr` is returned.
1020 /// The mask will be a vector of booleans representing meaningful vector
1021 /// elements in the current iteration. It is filled with ones for each iteration
1022 /// except for the last one, where it has the form `11...100...0` with the
1023 /// number of ones equal to the number of meaningful elements (i.e. the number
1024 /// of iterations that would be left in the original loop).
1025 static Value createMask(AffineForOp vecForOp, VectorizationState &state) {
1026  assert(state.strategy->vectorSizes.size() == 1 &&
1027  "Creating a mask non-1-D vectors is not supported.");
1028  assert(vecForOp.getStep() == state.strategy->vectorSizes[0] &&
1029  "Creating a mask for loops with non-unit original step size is not "
1030  "supported.");
1031 
1032  // Check if we have already created the mask.
1033  if (Value mask = state.vecLoopToMask.lookup(vecForOp))
1034  return mask;
1035 
1036  // If the loop has constant bounds and the original number of iterations is
1037  // divisable by the vector size then we don't need a mask.
1038  if (vecForOp.hasConstantBounds()) {
1039  int64_t originalTripCount =
1040  vecForOp.getConstantUpperBound() - vecForOp.getConstantLowerBound();
1041  if (originalTripCount % vecForOp.getStepAsInt() == 0)
1042  return nullptr;
1043  }
1044 
1045  OpBuilder::InsertionGuard guard(state.builder);
1046  state.builder.setInsertionPointToStart(vecForOp.getBody());
1047 
1048  // We generate the mask using the `vector.create_mask` operation which accepts
1049  // the number of meaningful elements (i.e. the length of the prefix of 1s).
1050  // To compute the number of meaningful elements we subtract the current value
1051  // of the iteration variable from the upper bound of the loop. Example:
1052  //
1053  // // 500 is the upper bound of the loop
1054  // #map = affine_map<(d0) -> (500 - d0)>
1055  // %elems_left = affine.apply #map(%iv)
1056  // %mask = vector.create_mask %elems_left : vector<128xi1>
1057 
1058  Location loc = vecForOp.getLoc();
1059 
1060  // First we get the upper bound of the loop using `affine.apply` or
1061  // `affine.min`.
1062  AffineMap ubMap = vecForOp.getUpperBoundMap();
1063  Value ub;
1064  if (ubMap.getNumResults() == 1)
1065  ub = state.builder.create<AffineApplyOp>(loc, vecForOp.getUpperBoundMap(),
1066  vecForOp.getUpperBoundOperands());
1067  else
1068  ub = state.builder.create<AffineMinOp>(loc, vecForOp.getUpperBoundMap(),
1069  vecForOp.getUpperBoundOperands());
1070  // Then we compute the number of (original) iterations left in the loop.
1071  AffineExpr subExpr =
1072  state.builder.getAffineDimExpr(0) - state.builder.getAffineDimExpr(1);
1073  Value itersLeft =
1074  makeComposedAffineApply(state.builder, loc, AffineMap::get(2, 0, subExpr),
1075  {ub, vecForOp.getInductionVar()});
1076  // If the affine maps were successfully composed then `ub` is unneeded.
1077  if (ub.use_empty())
1078  ub.getDefiningOp()->erase();
1079  // Finally we create the mask.
1080  Type maskTy = VectorType::get(state.strategy->vectorSizes,
1081  state.builder.getIntegerType(1));
1082  Value mask =
1083  state.builder.create<vector::CreateMaskOp>(loc, maskTy, itersLeft);
1084 
1085  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ creating a mask:\n"
1086  << itersLeft << "\n"
1087  << mask << "\n");
1088 
1089  state.vecLoopToMask[vecForOp] = mask;
1090  return mask;
1091 }
1092 
1093 /// Returns true if the provided value is vector uniform given the vectorization
1094 /// strategy.
1095 // TODO: For now, only values that are induction variables of loops not in
1096 // `loopToVectorDim` or invariants to all the loops in the vectorization
1097 // strategy are considered vector uniforms.
1098 static bool isUniformDefinition(Value value,
1099  const VectorizationStrategy *strategy) {
1100  AffineForOp forOp = getForInductionVarOwner(value);
1101  if (forOp && strategy->loopToVectorDim.count(forOp) == 0)
1102  return true;
1103 
1104  for (auto loopToDim : strategy->loopToVectorDim) {
1105  auto loop = cast<AffineForOp>(loopToDim.first);
1106  if (!loop.isDefinedOutsideOfLoop(value))
1107  return false;
1108  }
1109  return true;
1110 }
1111 
1112 /// Generates a broadcast op for the provided uniform value using the
1113 /// vectorization strategy in 'state'.
1114 static Operation *vectorizeUniform(Value uniformVal,
1115  VectorizationState &state) {
1116  OpBuilder::InsertionGuard guard(state.builder);
1117  Value uniformScalarRepl =
1118  state.valueScalarReplacement.lookupOrDefault(uniformVal);
1119  state.builder.setInsertionPointAfterValue(uniformScalarRepl);
1120 
1121  auto vectorTy = getVectorType(uniformVal.getType(), state.strategy);
1122  auto bcastOp = state.builder.create<BroadcastOp>(uniformVal.getLoc(),
1123  vectorTy, uniformScalarRepl);
1124  state.registerValueVectorReplacement(uniformVal, bcastOp);
1125  return bcastOp;
1126 }
1127 
1128 /// Tries to vectorize a given `operand` by applying the following logic:
1129 /// 1. if the defining operation has been already vectorized, `operand` is
1130 /// already in the proper vector form;
1131 /// 2. if the `operand` is a constant, returns the vectorized form of the
1132 /// constant;
1133 /// 3. if the `operand` is uniform, returns a vector broadcast of the `op`;
1134 /// 4. otherwise, the vectorization of `operand` is not supported.
1135 /// Newly created vector operations are registered in `state` as replacement
1136 /// for their scalar counterparts.
1137 /// In particular this logic captures some of the use cases where definitions
1138 /// that are not scoped under the current pattern are needed to vectorize.
1139 /// One such example is top level function constants that need to be splatted.
1140 ///
1141 /// Returns an operand that has been vectorized to match `state`'s strategy if
1142 /// vectorization is possible with the above logic. Returns nullptr otherwise.
1143 ///
1144 /// TODO: handle more complex cases.
1146  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ vectorize operand: " << operand);
1147  // If this value is already vectorized, we are done.
1148  if (Value vecRepl = state.valueVectorReplacement.lookupOrNull(operand)) {
1149  LLVM_DEBUG(dbgs() << " -> already vectorized: " << vecRepl);
1150  return vecRepl;
1151  }
1152 
1153  // An vector operand that is not in the replacement map should never reach
1154  // this point. Reaching this point could mean that the code was already
1155  // vectorized and we shouldn't try to vectorize already vectorized code.
1156  assert(!isa<VectorType>(operand.getType()) &&
1157  "Vector op not found in replacement map");
1158 
1159  // Vectorize constant.
1160  if (auto constOp = operand.getDefiningOp<arith::ConstantOp>()) {
1161  auto vecConstant = vectorizeConstant(constOp, state);
1162  LLVM_DEBUG(dbgs() << "-> constant: " << vecConstant);
1163  return vecConstant.getResult();
1164  }
1165 
1166  // Vectorize uniform values.
1167  if (isUniformDefinition(operand, state.strategy)) {
1168  Operation *vecUniform = vectorizeUniform(operand, state);
1169  LLVM_DEBUG(dbgs() << "-> uniform: " << *vecUniform);
1170  return vecUniform->getResult(0);
1171  }
1172 
1173  // Check for unsupported block argument scenarios. A supported block argument
1174  // should have been vectorized already.
1175  if (!operand.getDefiningOp())
1176  LLVM_DEBUG(dbgs() << "-> unsupported block argument\n");
1177  else
1178  // Generic unsupported case.
1179  LLVM_DEBUG(dbgs() << "-> non-vectorizable\n");
1180 
1181  return nullptr;
1182 }
1183 
1184 /// Vectorizes an affine load with the vectorization strategy in 'state' by
1185 /// generating a 'vector.transfer_read' op with the proper permutation map
1186 /// inferred from the indices of the load. The new 'vector.transfer_read' is
1187 /// registered as replacement of the scalar load. Returns the newly created
1188 /// 'vector.transfer_read' if vectorization was successful. Returns nullptr,
1189 /// otherwise.
1190 static Operation *vectorizeAffineLoad(AffineLoadOp loadOp,
1191  VectorizationState &state) {
1192  MemRefType memRefType = loadOp.getMemRefType();
1193  Type elementType = memRefType.getElementType();
1194  auto vectorType = VectorType::get(state.strategy->vectorSizes, elementType);
1195 
1196  // Replace map operands with operands from the vector loop nest.
1197  SmallVector<Value, 8> mapOperands;
1198  state.getScalarValueReplacementsFor(loadOp.getMapOperands(), mapOperands);
1199 
1200  // Compute indices for the transfer op. AffineApplyOp's may be generated.
1201  SmallVector<Value, 8> indices;
1202  indices.reserve(memRefType.getRank());
1203  if (loadOp.getAffineMap() !=
1204  state.builder.getMultiDimIdentityMap(memRefType.getRank())) {
1205  // Check the operand in loadOp affine map does not come from AffineApplyOp.
1206  for (auto op : mapOperands) {
1207  if (op.getDefiningOp<AffineApplyOp>())
1208  return nullptr;
1209  }
1210  computeMemoryOpIndices(loadOp, loadOp.getAffineMap(), mapOperands, state,
1211  indices);
1212  } else {
1213  indices.append(mapOperands.begin(), mapOperands.end());
1214  }
1215 
1216  // Compute permutation map using the information of new vector loops.
1217  auto permutationMap = makePermutationMap(state.builder.getInsertionBlock(),
1218  indices, state.vecLoopToVecDim);
1219  if (!permutationMap) {
1220  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ can't compute permutationMap\n");
1221  return nullptr;
1222  }
1223  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ permutationMap: ");
1224  LLVM_DEBUG(permutationMap.print(dbgs()));
1225 
1226  auto transfer = state.builder.create<vector::TransferReadOp>(
1227  loadOp.getLoc(), vectorType, loadOp.getMemRef(), indices, permutationMap);
1228 
1229  // Register replacement for future uses in the scope.
1230  state.registerOpVectorReplacement(loadOp, transfer);
1231  return transfer;
1232 }
1233 
1234 /// Vectorizes an affine store with the vectorization strategy in 'state' by
1235 /// generating a 'vector.transfer_write' op with the proper permutation map
1236 /// inferred from the indices of the store. The new 'vector.transfer_store' is
1237 /// registered as replacement of the scalar load. Returns the newly created
1238 /// 'vector.transfer_write' if vectorization was successful. Returns nullptr,
1239 /// otherwise.
1240 static Operation *vectorizeAffineStore(AffineStoreOp storeOp,
1241  VectorizationState &state) {
1242  MemRefType memRefType = storeOp.getMemRefType();
1243  Value vectorValue = vectorizeOperand(storeOp.getValueToStore(), state);
1244  if (!vectorValue)
1245  return nullptr;
1246 
1247  // Replace map operands with operands from the vector loop nest.
1248  SmallVector<Value, 8> mapOperands;
1249  state.getScalarValueReplacementsFor(storeOp.getMapOperands(), mapOperands);
1250 
1251  // Compute indices for the transfer op. AffineApplyOp's may be generated.
1252  SmallVector<Value, 8> indices;
1253  indices.reserve(memRefType.getRank());
1254  if (storeOp.getAffineMap() !=
1255  state.builder.getMultiDimIdentityMap(memRefType.getRank()))
1256  computeMemoryOpIndices(storeOp, storeOp.getAffineMap(), mapOperands, state,
1257  indices);
1258  else
1259  indices.append(mapOperands.begin(), mapOperands.end());
1260 
1261  // Compute permutation map using the information of new vector loops.
1262  auto permutationMap = makePermutationMap(state.builder.getInsertionBlock(),
1263  indices, state.vecLoopToVecDim);
1264  if (!permutationMap)
1265  return nullptr;
1266  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ permutationMap: ");
1267  LLVM_DEBUG(permutationMap.print(dbgs()));
1268 
1269  auto transfer = state.builder.create<vector::TransferWriteOp>(
1270  storeOp.getLoc(), vectorValue, storeOp.getMemRef(), indices,
1271  permutationMap);
1272  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ vectorized store: " << transfer);
1273 
1274  // Register replacement for future uses in the scope.
1275  state.registerOpVectorReplacement(storeOp, transfer);
1276  return transfer;
1277 }
1278 
1279 /// Returns true if `value` is a constant equal to the neutral element of the
1280 /// given vectorizable reduction.
1281 static bool isNeutralElementConst(arith::AtomicRMWKind reductionKind,
1282  Value value, VectorizationState &state) {
1283  Type scalarTy = value.getType();
1284  if (!VectorType::isValidElementType(scalarTy))
1285  return false;
1286  Attribute valueAttr = getIdentityValueAttr(reductionKind, scalarTy,
1287  state.builder, value.getLoc());
1288  if (auto constOp = dyn_cast_or_null<arith::ConstantOp>(value.getDefiningOp()))
1289  return constOp.getValue() == valueAttr;
1290  return false;
1291 }
1292 
1293 /// Vectorizes a loop with the vectorization strategy in 'state'. A new loop is
1294 /// created and registered as replacement for the scalar loop. The builder's
1295 /// insertion point is set to the new loop's body so that subsequent vectorized
1296 /// operations are inserted into the new loop. If the loop is a vector
1297 /// dimension, the step of the newly created loop will reflect the vectorization
1298 /// factor used to vectorized that dimension.
1299 static Operation *vectorizeAffineForOp(AffineForOp forOp,
1300  VectorizationState &state) {
1301  const VectorizationStrategy &strategy = *state.strategy;
1302  auto loopToVecDimIt = strategy.loopToVectorDim.find(forOp);
1303  bool isLoopVecDim = loopToVecDimIt != strategy.loopToVectorDim.end();
1304 
1305  // TODO: Vectorization of reduction loops is not supported for non-unit steps.
1306  if (isLoopVecDim && forOp.getNumIterOperands() > 0 && forOp.getStep() != 1) {
1307  LLVM_DEBUG(
1308  dbgs()
1309  << "\n[early-vect]+++++ unsupported step size for reduction loop: "
1310  << forOp.getStep() << "\n");
1311  return nullptr;
1312  }
1313 
1314  // If we are vectorizing a vector dimension, compute a new step for the new
1315  // vectorized loop using the vectorization factor for the vector dimension.
1316  // Otherwise, propagate the step of the scalar loop.
1317  unsigned newStep;
1318  if (isLoopVecDim) {
1319  unsigned vectorDim = loopToVecDimIt->second;
1320  assert(vectorDim < strategy.vectorSizes.size() && "vector dim overflow");
1321  int64_t forOpVecFactor = strategy.vectorSizes[vectorDim];
1322  newStep = forOp.getStepAsInt() * forOpVecFactor;
1323  } else {
1324  newStep = forOp.getStepAsInt();
1325  }
1326 
1327  // Get information about reduction kinds.
1328  ArrayRef<LoopReduction> reductions;
1329  if (isLoopVecDim && forOp.getNumIterOperands() > 0) {
1330  auto it = strategy.reductionLoops.find(forOp);
1331  assert(it != strategy.reductionLoops.end() &&
1332  "Reduction descriptors not found when vectorizing a reduction loop");
1333  reductions = it->second;
1334  assert(reductions.size() == forOp.getNumIterOperands() &&
1335  "The size of reductions array must match the number of iter_args");
1336  }
1337 
1338  // Vectorize 'iter_args'.
1339  SmallVector<Value, 8> vecIterOperands;
1340  if (!isLoopVecDim) {
1341  for (auto operand : forOp.getInits())
1342  vecIterOperands.push_back(vectorizeOperand(operand, state));
1343  } else {
1344  // For reduction loops we need to pass a vector of neutral elements as an
1345  // initial value of the accumulator. We will add the original initial value
1346  // later.
1347  for (auto redAndOperand : llvm::zip(reductions, forOp.getInits())) {
1348  vecIterOperands.push_back(createInitialVector(
1349  std::get<0>(redAndOperand).kind, std::get<1>(redAndOperand), state));
1350  }
1351  }
1352 
1353  auto vecForOp = state.builder.create<AffineForOp>(
1354  forOp.getLoc(), forOp.getLowerBoundOperands(), forOp.getLowerBoundMap(),
1355  forOp.getUpperBoundOperands(), forOp.getUpperBoundMap(), newStep,
1356  vecIterOperands,
1357  /*bodyBuilder=*/[](OpBuilder &, Location, Value, ValueRange) {
1358  // Make sure we don't create a default terminator in the loop body as
1359  // the proper terminator will be added during vectorization.
1360  });
1361 
1362  // Register loop-related replacements:
1363  // 1) The new vectorized loop is registered as vector replacement of the
1364  // scalar loop.
1365  // 2) The new iv of the vectorized loop is registered as scalar replacement
1366  // since a scalar copy of the iv will prevail in the vectorized loop.
1367  // TODO: A vector replacement will also be added in the future when
1368  // vectorization of linear ops is supported.
1369  // 3) The new 'iter_args' region arguments are registered as vector
1370  // replacements since they have been vectorized.
1371  // 4) If the loop performs a reduction along the vector dimension, a
1372  // `vector.reduction` or similar op is inserted for each resulting value
1373  // of the loop and its scalar value replaces the corresponding scalar
1374  // result of the loop.
1375  state.registerOpVectorReplacement(forOp, vecForOp);
1376  state.registerValueScalarReplacement(forOp.getInductionVar(),
1377  vecForOp.getInductionVar());
1378  for (auto iterTuple :
1379  llvm ::zip(forOp.getRegionIterArgs(), vecForOp.getRegionIterArgs()))
1380  state.registerBlockArgVectorReplacement(std::get<0>(iterTuple),
1381  std::get<1>(iterTuple));
1382 
1383  if (isLoopVecDim) {
1384  for (unsigned i = 0; i < vecForOp.getNumIterOperands(); ++i) {
1385  // First, we reduce the vector returned from the loop into a scalar.
1386  Value reducedRes =
1387  getVectorReductionOp(reductions[i].kind, state.builder,
1388  vecForOp.getLoc(), vecForOp.getResult(i));
1389  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ creating a vector reduction: "
1390  << reducedRes);
1391  // Then we combine it with the original (scalar) initial value unless it
1392  // is equal to the neutral element of the reduction.
1393  Value origInit = forOp.getOperand(forOp.getNumControlOperands() + i);
1394  Value finalRes = reducedRes;
1395  if (!isNeutralElementConst(reductions[i].kind, origInit, state))
1396  finalRes =
1397  arith::getReductionOp(reductions[i].kind, state.builder,
1398  reducedRes.getLoc(), reducedRes, origInit);
1399  state.registerLoopResultScalarReplacement(forOp.getResult(i), finalRes);
1400  }
1401  }
1402 
1403  if (isLoopVecDim)
1404  state.vecLoopToVecDim[vecForOp] = loopToVecDimIt->second;
1405 
1406  // Change insertion point so that upcoming vectorized instructions are
1407  // inserted into the vectorized loop's body.
1408  state.builder.setInsertionPointToStart(vecForOp.getBody());
1409 
1410  // If this is a reduction loop then we may need to create a mask to filter out
1411  // garbage in the last iteration.
1412  if (isLoopVecDim && forOp.getNumIterOperands() > 0)
1413  createMask(vecForOp, state);
1414 
1415  return vecForOp;
1416 }
1417 
1418 /// Vectorizes arbitrary operation by plain widening. We apply generic type
1419 /// widening of all its results and retrieve the vector counterparts for all its
1420 /// operands.
1422  SmallVector<Type, 8> vectorTypes;
1423  for (Value result : op->getResults())
1424  vectorTypes.push_back(
1425  VectorType::get(state.strategy->vectorSizes, result.getType()));
1426 
1427  SmallVector<Value, 8> vectorOperands;
1428  for (Value operand : op->getOperands()) {
1429  Value vecOperand = vectorizeOperand(operand, state);
1430  if (!vecOperand) {
1431  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ an operand failed vectorize\n");
1432  return nullptr;
1433  }
1434  vectorOperands.push_back(vecOperand);
1435  }
1436 
1437  // Create a clone of the op with the proper operands and return types.
1438  // TODO: The following assumes there is always an op with a fixed
1439  // name that works both in scalar mode and vector mode.
1440  // TODO: Is it worth considering an Operation.clone operation which
1441  // changes the type so we can promote an Operation with less boilerplate?
1442  Operation *vecOp =
1443  state.builder.create(op->getLoc(), op->getName().getIdentifier(),
1444  vectorOperands, vectorTypes, op->getAttrs());
1445  state.registerOpVectorReplacement(op, vecOp);
1446  return vecOp;
1447 }
1448 
1449 /// Vectorizes a yield operation by widening its types. The builder's insertion
1450 /// point is set after the vectorized parent op to continue vectorizing the
1451 /// operations after the parent op. When vectorizing a reduction loop a mask may
1452 /// be used to prevent adding garbage values to the accumulator.
1453 static Operation *vectorizeAffineYieldOp(AffineYieldOp yieldOp,
1454  VectorizationState &state) {
1455  Operation *newYieldOp = widenOp(yieldOp, state);
1456  Operation *newParentOp = state.builder.getInsertionBlock()->getParentOp();
1457 
1458  // If there is a mask for this loop then we must prevent garbage values from
1459  // being added to the accumulator by inserting `select` operations, for
1460  // example:
1461  //
1462  // %val_masked = select %mask, %val, %neutralCst : vector<128xi1>,
1463  // vector<128xf32>
1464  // %res = arith.addf %acc, %val_masked : vector<128xf32>
1465  // affine.yield %res : vector<128xf32>
1466  //
1467  if (Value mask = state.vecLoopToMask.lookup(newParentOp)) {
1468  state.builder.setInsertionPoint(newYieldOp);
1469  for (unsigned i = 0; i < newYieldOp->getNumOperands(); ++i) {
1470  SmallVector<Operation *> combinerOps;
1471  Value reducedVal = matchReduction(
1472  cast<AffineForOp>(newParentOp).getRegionIterArgs(), i, combinerOps);
1473  assert(reducedVal && "expect non-null value for parallel reduction loop");
1474  assert(combinerOps.size() == 1 && "expect only one combiner op");
1475  // IterOperands are neutral element vectors.
1476  Value neutralVal = cast<AffineForOp>(newParentOp).getInits()[i];
1477  state.builder.setInsertionPoint(combinerOps.back());
1478  Value maskedReducedVal = state.builder.create<arith::SelectOp>(
1479  reducedVal.getLoc(), mask, reducedVal, neutralVal);
1480  LLVM_DEBUG(
1481  dbgs() << "\n[early-vect]+++++ masking an input to a binary op that"
1482  "produces value for a yield Op: "
1483  << maskedReducedVal);
1484  combinerOps.back()->replaceUsesOfWith(reducedVal, maskedReducedVal);
1485  }
1486  }
1487 
1488  state.builder.setInsertionPointAfter(newParentOp);
1489  return newYieldOp;
1490 }
1491 
1492 /// Encodes Operation-specific behavior for vectorization. In general we
1493 /// assume that all operands of an op must be vectorized but this is not
1494 /// always true. In the future, it would be nice to have a trait that
1495 /// describes how a particular operation vectorizes. For now we implement the
1496 /// case distinction here. Returns a vectorized form of an operation or
1497 /// nullptr if vectorization fails.
1498 // TODO: consider adding a trait to Op to describe how it gets vectorized.
1499 // Maybe some Ops are not vectorizable or require some tricky logic, we cannot
1500 // do one-off logic here; ideally it would be TableGen'd.
1502  VectorizationState &state) {
1503  // Sanity checks.
1504  assert(!isa<vector::TransferReadOp>(op) &&
1505  "vector.transfer_read cannot be further vectorized");
1506  assert(!isa<vector::TransferWriteOp>(op) &&
1507  "vector.transfer_write cannot be further vectorized");
1508 
1509  if (auto loadOp = dyn_cast<AffineLoadOp>(op))
1510  return vectorizeAffineLoad(loadOp, state);
1511  if (auto storeOp = dyn_cast<AffineStoreOp>(op))
1512  return vectorizeAffineStore(storeOp, state);
1513  if (auto forOp = dyn_cast<AffineForOp>(op))
1514  return vectorizeAffineForOp(forOp, state);
1515  if (auto yieldOp = dyn_cast<AffineYieldOp>(op))
1516  return vectorizeAffineYieldOp(yieldOp, state);
1517  if (auto constant = dyn_cast<arith::ConstantOp>(op))
1518  return vectorizeConstant(constant, state);
1519  if (auto applyOp = dyn_cast<AffineApplyOp>(op))
1520  return vectorizeAffineApplyOp(applyOp, state);
1521 
1522  // Other ops with regions are not supported.
1523  if (op->getNumRegions() != 0)
1524  return nullptr;
1525 
1526  return widenOp(op, state);
1527 }
1528 
1529 /// Recursive implementation to convert all the nested loops in 'match' to a 2D
1530 /// vector container that preserves the relative nesting level of each loop with
1531 /// respect to the others in 'match'. 'currentLevel' is the nesting level that
1532 /// will be assigned to the loop in the current 'match'.
1533 static void
1534 getMatchedAffineLoopsRec(NestedMatch match, unsigned currentLevel,
1535  std::vector<SmallVector<AffineForOp, 2>> &loops) {
1536  // Add a new empty level to the output if it doesn't exist already.
1537  assert(currentLevel <= loops.size() && "Unexpected currentLevel");
1538  if (currentLevel == loops.size())
1539  loops.emplace_back();
1540 
1541  // Add current match and recursively visit its children.
1542  loops[currentLevel].push_back(cast<AffineForOp>(match.getMatchedOperation()));
1543  for (auto childMatch : match.getMatchedChildren()) {
1544  getMatchedAffineLoopsRec(childMatch, currentLevel + 1, loops);
1545  }
1546 }
1547 
1548 /// Converts all the nested loops in 'match' to a 2D vector container that
1549 /// preserves the relative nesting level of each loop with respect to the others
1550 /// in 'match'. This means that every loop in 'loops[i]' will have a parent loop
1551 /// in 'loops[i-1]'. A loop in 'loops[i]' may or may not have a child loop in
1552 /// 'loops[i+1]'.
1553 static void
1555  std::vector<SmallVector<AffineForOp, 2>> &loops) {
1556  getMatchedAffineLoopsRec(match, /*currLoopDepth=*/0, loops);
1557 }
1558 
1559 /// Internal implementation to vectorize affine loops from a single loop nest
1560 /// using an n-D vectorization strategy.
1561 static LogicalResult
1563  const VectorizationStrategy &strategy) {
1564  assert(loops[0].size() == 1 && "Expected single root loop");
1565  AffineForOp rootLoop = loops[0][0];
1566  VectorizationState state(rootLoop.getContext());
1567  state.builder.setInsertionPointAfter(rootLoop);
1568  state.strategy = &strategy;
1569 
1570  // Since patterns are recursive, they can very well intersect.
1571  // Since we do not want a fully greedy strategy in general, we decouple
1572  // pattern matching, from profitability analysis, from application.
1573  // As a consequence we must check that each root pattern is still
1574  // vectorizable. If a pattern is not vectorizable anymore, we just skip it.
1575  // TODO: implement a non-greedy profitability analysis that keeps only
1576  // non-intersecting patterns.
1577  if (!isVectorizableLoopBody(rootLoop, vectorTransferPattern())) {
1578  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ loop is not vectorizable");
1579  return failure();
1580  }
1581 
1582  //////////////////////////////////////////////////////////////////////////////
1583  // Vectorize the scalar loop nest following a topological order. A new vector
1584  // loop nest with the vectorized operations is created along the process. If
1585  // vectorization succeeds, the scalar loop nest is erased. If vectorization
1586  // fails, the vector loop nest is erased and the scalar loop nest is not
1587  // modified.
1588  //////////////////////////////////////////////////////////////////////////////
1589 
1590  auto opVecResult = rootLoop.walk<WalkOrder::PreOrder>([&](Operation *op) {
1591  LLVM_DEBUG(dbgs() << "[early-vect]+++++ Vectorizing: " << *op);
1592  Operation *vectorOp = vectorizeOneOperation(op, state);
1593  if (!vectorOp) {
1594  LLVM_DEBUG(
1595  dbgs() << "[early-vect]+++++ failed vectorizing the operation: "
1596  << *op << "\n");
1597  return WalkResult::interrupt();
1598  }
1599 
1600  return WalkResult::advance();
1601  });
1602 
1603  if (opVecResult.wasInterrupted()) {
1604  LLVM_DEBUG(dbgs() << "[early-vect]+++++ failed vectorization for: "
1605  << rootLoop << "\n");
1606  // Erase vector loop nest if it was created.
1607  auto vecRootLoopIt = state.opVectorReplacement.find(rootLoop);
1608  if (vecRootLoopIt != state.opVectorReplacement.end())
1609  eraseLoopNest(cast<AffineForOp>(vecRootLoopIt->second));
1610 
1611  return failure();
1612  }
1613 
1614  // Replace results of reduction loops with the scalar values computed using
1615  // `vector.reduce` or similar ops.
1616  for (auto resPair : state.loopResultScalarReplacement)
1617  resPair.first.replaceAllUsesWith(resPair.second);
1618 
1619  assert(state.opVectorReplacement.count(rootLoop) == 1 &&
1620  "Expected vector replacement for loop nest");
1621  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ success vectorizing pattern");
1622  LLVM_DEBUG(dbgs() << "\n[early-vect]+++++ vectorization result:\n"
1623  << *state.opVectorReplacement[rootLoop]);
1624 
1625  // Finish this vectorization pattern.
1626  state.finishVectorizationPattern(rootLoop);
1627  return success();
1628 }
1629 
1630 /// Extracts the matched loops and vectorizes them following a topological
1631 /// order. A new vector loop nest will be created if vectorization succeeds. The
1632 /// original loop nest won't be modified in any case.
1634  const VectorizationStrategy &strategy) {
1635  std::vector<SmallVector<AffineForOp, 2>> loopsToVectorize;
1636  getMatchedAffineLoops(m, loopsToVectorize);
1637  return vectorizeLoopNest(loopsToVectorize, strategy);
1638 }
1639 
1640 /// Traverses all the loop matches and classifies them into intersection
1641 /// buckets. Two matches intersect if any of them encloses the other one. A
1642 /// match intersects with a bucket if the match intersects with the root
1643 /// (outermost) loop in that bucket.
1645  ArrayRef<NestedMatch> matches,
1646  std::vector<SmallVector<NestedMatch, 8>> &intersectionBuckets) {
1647  assert(intersectionBuckets.empty() && "Expected empty output");
1648  // Keeps track of the root (outermost) loop of each bucket.
1649  SmallVector<AffineForOp, 8> bucketRoots;
1650 
1651  for (const NestedMatch &match : matches) {
1652  AffineForOp matchRoot = cast<AffineForOp>(match.getMatchedOperation());
1653  bool intersects = false;
1654  for (int i = 0, end = intersectionBuckets.size(); i < end; ++i) {
1655  AffineForOp bucketRoot = bucketRoots[i];
1656  // Add match to the bucket if the bucket root encloses the match root.
1657  if (bucketRoot->isAncestor(matchRoot)) {
1658  intersectionBuckets[i].push_back(match);
1659  intersects = true;
1660  break;
1661  }
1662  // Add match to the bucket if the match root encloses the bucket root. The
1663  // match root becomes the new bucket root.
1664  if (matchRoot->isAncestor(bucketRoot)) {
1665  bucketRoots[i] = matchRoot;
1666  intersectionBuckets[i].push_back(match);
1667  intersects = true;
1668  break;
1669  }
1670  }
1671 
1672  // Match doesn't intersect with any existing bucket. Create a new bucket for
1673  // it.
1674  if (!intersects) {
1675  bucketRoots.push_back(matchRoot);
1676  intersectionBuckets.emplace_back();
1677  intersectionBuckets.back().push_back(match);
1678  }
1679  }
1680 }
1681 
1682 /// Internal implementation to vectorize affine loops in 'loops' using the n-D
1683 /// vectorization factors in 'vectorSizes'. By default, each vectorization
1684 /// factor is applied inner-to-outer to the loops of each loop nest.
1685 /// 'fastestVaryingPattern' can be optionally used to provide a different loop
1686 /// vectorization order. `reductionLoops` can be provided to specify loops which
1687 /// can be vectorized along the reduction dimension.
1688 static void vectorizeLoops(Operation *parentOp, DenseSet<Operation *> &loops,
1689  ArrayRef<int64_t> vectorSizes,
1690  ArrayRef<int64_t> fastestVaryingPattern,
1691  const ReductionLoopMap &reductionLoops) {
1692  assert((reductionLoops.empty() || vectorSizes.size() == 1) &&
1693  "Vectorizing reductions is supported only for 1-D vectors");
1694 
1695  // Compute 1-D, 2-D or 3-D loop pattern to be matched on the target loops.
1696  std::optional<NestedPattern> pattern =
1697  makePattern(loops, vectorSizes.size(), fastestVaryingPattern);
1698  if (!pattern) {
1699  LLVM_DEBUG(dbgs() << "\n[early-vect] pattern couldn't be computed\n");
1700  return;
1701  }
1702 
1703  LLVM_DEBUG(dbgs() << "\n******************************************");
1704  LLVM_DEBUG(dbgs() << "\n******************************************");
1705  LLVM_DEBUG(dbgs() << "\n[early-vect] new pattern on parent op\n");
1706  LLVM_DEBUG(dbgs() << *parentOp << "\n");
1707 
1708  unsigned patternDepth = pattern->getDepth();
1709 
1710  // Compute all the pattern matches and classify them into buckets of
1711  // intersecting matches.
1712  SmallVector<NestedMatch, 32> allMatches;
1713  pattern->match(parentOp, &allMatches);
1714  std::vector<SmallVector<NestedMatch, 8>> intersectionBuckets;
1715  computeIntersectionBuckets(allMatches, intersectionBuckets);
1716 
1717  // Iterate over all buckets and vectorize the matches eagerly. We can only
1718  // vectorize one match from each bucket since all the matches within a bucket
1719  // intersect.
1720  for (auto &intersectingMatches : intersectionBuckets) {
1721  for (NestedMatch &match : intersectingMatches) {
1722  VectorizationStrategy strategy;
1723  // TODO: depending on profitability, elect to reduce the vector size.
1724  strategy.vectorSizes.assign(vectorSizes.begin(), vectorSizes.end());
1725  strategy.reductionLoops = reductionLoops;
1726  if (failed(analyzeProfitability(match.getMatchedChildren(), 1,
1727  patternDepth, &strategy))) {
1728  continue;
1729  }
1730  vectorizeLoopIfProfitable(match.getMatchedOperation(), 0, patternDepth,
1731  &strategy);
1732  // Vectorize match. Skip the rest of intersecting matches in the bucket if
1733  // vectorization succeeded.
1734  // TODO: if pattern does not apply, report it; alter the cost/benefit.
1735  // TODO: some diagnostics if failure to vectorize occurs.
1736  if (succeeded(vectorizeRootMatch(match, strategy)))
1737  break;
1738  }
1739  }
1740 
1741  LLVM_DEBUG(dbgs() << "\n");
1742 }
1743 
1744 /// Applies vectorization to the current function by searching over a bunch of
1745 /// predetermined patterns.
1746 void Vectorize::runOnOperation() {
1747  func::FuncOp f = getOperation();
1748  if (!fastestVaryingPattern.empty() &&
1749  fastestVaryingPattern.size() != vectorSizes.size()) {
1750  f.emitRemark("Fastest varying pattern specified with different size than "
1751  "the vector size.");
1752  return signalPassFailure();
1753  }
1754 
1755  if (vectorizeReductions && vectorSizes.size() != 1) {
1756  f.emitError("Vectorizing reductions is supported only for 1-D vectors.");
1757  return signalPassFailure();
1758  }
1759 
1760  if (llvm::any_of(vectorSizes, [](int64_t size) { return size <= 0; })) {
1761  f.emitError("Vectorization factor must be greater than zero.");
1762  return signalPassFailure();
1763  }
1764 
1765  DenseSet<Operation *> parallelLoops;
1766  ReductionLoopMap reductionLoops;
1767 
1768  // If 'vectorize-reduction=true' is provided, we also populate the
1769  // `reductionLoops` map.
1770  if (vectorizeReductions) {
1771  f.walk([&parallelLoops, &reductionLoops](AffineForOp loop) {
1772  SmallVector<LoopReduction, 2> reductions;
1773  if (isLoopParallel(loop, &reductions)) {
1774  parallelLoops.insert(loop);
1775  // If it's not a reduction loop, adding it to the map is not necessary.
1776  if (!reductions.empty())
1777  reductionLoops[loop] = reductions;
1778  }
1779  });
1780  } else {
1781  f.walk([&parallelLoops](AffineForOp loop) {
1782  if (isLoopParallel(loop))
1783  parallelLoops.insert(loop);
1784  });
1785  }
1786 
1787  // Thread-safe RAII local context, BumpPtrAllocator freed on exit.
1788  NestedPatternContext mlContext;
1789  vectorizeLoops(f, parallelLoops, vectorSizes, fastestVaryingPattern,
1790  reductionLoops);
1791 }
1792 
1793 /// Verify that affine loops in 'loops' meet the nesting criteria expected by
1794 /// SuperVectorizer:
1795 /// * There must be at least one loop.
1796 /// * There must be a single root loop (nesting level 0).
1797 /// * Each loop at a given nesting level must be nested in a loop from a
1798 /// previous nesting level.
1799 static LogicalResult
1801  // Expected at least one loop.
1802  if (loops.empty())
1803  return failure();
1804 
1805  // Expected only one root loop.
1806  if (loops[0].size() != 1)
1807  return failure();
1808 
1809  // Traverse loops outer-to-inner to check some invariants.
1810  for (int i = 1, end = loops.size(); i < end; ++i) {
1811  for (AffineForOp loop : loops[i]) {
1812  // Check that each loop at this level is nested in one of the loops from
1813  // the previous level.
1814  if (none_of(loops[i - 1], [&](AffineForOp maybeParent) {
1815  return maybeParent->isProperAncestor(loop);
1816  }))
1817  return failure();
1818 
1819  // Check that each loop at this level is not nested in another loop from
1820  // this level.
1821  for (AffineForOp sibling : loops[i]) {
1822  if (sibling->isProperAncestor(loop))
1823  return failure();
1824  }
1825  }
1826  }
1827 
1828  return success();
1829 }
1830 
1831 
1832 /// External utility to vectorize affine loops in 'loops' using the n-D
1833 /// vectorization factors in 'vectorSizes'. By default, each vectorization
1834 /// factor is applied inner-to-outer to the loops of each loop nest.
1835 /// 'fastestVaryingPattern' can be optionally used to provide a different loop
1836 /// vectorization order.
1837 /// If `reductionLoops` is not empty, the given reduction loops may be
1838 /// vectorized along the reduction dimension.
1839 /// TODO: Vectorizing reductions is supported only for 1-D vectorization.
1841  Operation *parentOp, DenseSet<Operation *> &loops,
1842  ArrayRef<int64_t> vectorSizes, ArrayRef<int64_t> fastestVaryingPattern,
1843  const ReductionLoopMap &reductionLoops) {
1844  // Thread-safe RAII local context, BumpPtrAllocator freed on exit.
1845  NestedPatternContext mlContext;
1846  vectorizeLoops(parentOp, loops, vectorSizes, fastestVaryingPattern,
1847  reductionLoops);
1848 }
1849 
1850 /// External utility to vectorize affine loops from a single loop nest using an
1851 /// n-D vectorization strategy (see doc in VectorizationStrategy definition).
1852 /// Loops are provided in a 2D vector container. The first dimension represents
1853 /// the nesting level relative to the loops to be vectorized. The second
1854 /// dimension contains the loops. This means that:
1855 /// a) every loop in 'loops[i]' must have a parent loop in 'loops[i-1]',
1856 /// b) a loop in 'loops[i]' may or may not have a child loop in 'loops[i+1]'.
1857 ///
1858 /// For example, for the following loop nest:
1859 ///
1860 /// func @vec2d(%in0: memref<64x128x512xf32>, %in1: memref<64x128x128xf32>,
1861 /// %out0: memref<64x128x512xf32>,
1862 /// %out1: memref<64x128x128xf32>) {
1863 /// affine.for %i0 = 0 to 64 {
1864 /// affine.for %i1 = 0 to 128 {
1865 /// affine.for %i2 = 0 to 512 {
1866 /// %ld = affine.load %in0[%i0, %i1, %i2] : memref<64x128x512xf32>
1867 /// affine.store %ld, %out0[%i0, %i1, %i2] : memref<64x128x512xf32>
1868 /// }
1869 /// affine.for %i3 = 0 to 128 {
1870 /// %ld = affine.load %in1[%i0, %i1, %i3] : memref<64x128x128xf32>
1871 /// affine.store %ld, %out1[%i0, %i1, %i3] : memref<64x128x128xf32>
1872 /// }
1873 /// }
1874 /// }
1875 /// return
1876 /// }
1877 ///
1878 /// loops = {{%i0}, {%i2, %i3}}, to vectorize the outermost and the two
1879 /// innermost loops;
1880 /// loops = {{%i1}, {%i2, %i3}}, to vectorize the middle and the two innermost
1881 /// loops;
1882 /// loops = {{%i2}}, to vectorize only the first innermost loop;
1883 /// loops = {{%i3}}, to vectorize only the second innermost loop;
1884 /// loops = {{%i1}}, to vectorize only the middle loop.
1886  std::vector<SmallVector<AffineForOp, 2>> &loops,
1887  const VectorizationStrategy &strategy) {
1888  // Thread-safe RAII local context, BumpPtrAllocator freed on exit.
1889  NestedPatternContext mlContext;
1890  if (failed(verifyLoopNesting(loops)))
1891  return failure();
1892  return vectorizeLoopNest(loops, strategy);
1893 }
static Operation * vectorizeAffineStore(AffineStoreOp storeOp, VectorizationState &state)
Vectorizes an affine store with the vectorization strategy in 'state' by generating a 'vector....
static Operation * vectorizeAffineForOp(AffineForOp forOp, VectorizationState &state)
Vectorizes a loop with the vectorization strategy in 'state'.
static LogicalResult vectorizeRootMatch(NestedMatch m, const VectorizationStrategy &strategy)
Extracts the matched loops and vectorizes them following a topological order.
static LogicalResult verifyLoopNesting(const std::vector< SmallVector< AffineForOp, 2 >> &loops)
Verify that affine loops in 'loops' meet the nesting criteria expected by SuperVectorizer:
static void getMatchedAffineLoopsRec(NestedMatch match, unsigned currentLevel, std::vector< SmallVector< AffineForOp, 2 >> &loops)
Recursive implementation to convert all the nested loops in 'match' to a 2D vector container that pre...
static void vectorizeLoopIfProfitable(Operation *loop, unsigned depthInPattern, unsigned patternDepth, VectorizationStrategy *strategy)
static Operation * vectorizeOneOperation(Operation *op, VectorizationState &state)
Encodes Operation-specific behavior for vectorization.
static bool isNeutralElementConst(arith::AtomicRMWKind reductionKind, Value value, VectorizationState &state)
Returns true if value is a constant equal to the neutral element of the given vectorizable reduction.
static Operation * vectorizeUniform(Value uniformVal, VectorizationState &state)
Generates a broadcast op for the provided uniform value using the vectorization strategy in 'state'.
static Operation * vectorizeAffineYieldOp(AffineYieldOp yieldOp, VectorizationState &state)
Vectorizes a yield operation by widening its types.
static void computeIntersectionBuckets(ArrayRef< NestedMatch > matches, std::vector< SmallVector< NestedMatch, 8 >> &intersectionBuckets)
Traverses all the loop matches and classifies them into intersection buckets.
static LogicalResult analyzeProfitability(ArrayRef< NestedMatch > matches, unsigned depthInPattern, unsigned patternDepth, VectorizationStrategy *strategy)
Implements a simple strawman strategy for vectorization.
static FilterFunctionType isVectorizableLoopPtrFactory(const DenseSet< Operation * > &parallelLoops, int fastestVaryingMemRefDimension)
Forward declaration.
static Operation * widenOp(Operation *op, VectorizationState &state)
Vectorizes arbitrary operation by plain widening.
static arith::ConstantOp vectorizeConstant(arith::ConstantOp constOp, VectorizationState &state)
Tries to transform a scalar constant into a vector constant.
static bool isUniformDefinition(Value value, const VectorizationStrategy *strategy)
Returns true if the provided value is vector uniform given the vectorization strategy.
static void eraseLoopNest(AffineForOp forOp)
Erases a loop nest, including all its nested operations.
static VectorType getVectorType(Type scalarTy, const VectorizationStrategy *strategy)
Returns the vector type resulting from applying the provided vectorization strategy on the scalar typ...
static void getMatchedAffineLoops(NestedMatch match, std::vector< SmallVector< AffineForOp, 2 >> &loops)
Converts all the nested loops in 'match' to a 2D vector container that preserves the relative nesting...
static Value vectorizeOperand(Value operand, VectorizationState &state)
Tries to vectorize a given operand by applying the following logic:
static arith::ConstantOp createInitialVector(arith::AtomicRMWKind reductionKind, Value oldOperand, VectorizationState &state)
Creates a constant vector filled with the neutral elements of the given reduction.
static LogicalResult vectorizeLoopNest(std::vector< SmallVector< AffineForOp, 2 >> &loops, const VectorizationStrategy &strategy)
Internal implementation to vectorize affine loops from a single loop nest using an n-D vectorization ...
static NestedPattern & vectorTransferPattern()
static Operation * vectorizeAffineApplyOp(AffineApplyOp applyOp, VectorizationState &state)
We have no need to vectorize affine.apply.
static void vectorizeLoops(Operation *parentOp, DenseSet< Operation * > &loops, ArrayRef< int64_t > vectorSizes, ArrayRef< int64_t > fastestVaryingPattern, const ReductionLoopMap &reductionLoops)
Internal implementation to vectorize affine loops in 'loops' using the n-D vectorization factors in '...
static void computeMemoryOpIndices(Operation *op, AffineMap map, ValueRange mapOperands, VectorizationState &state, SmallVectorImpl< Value > &results)
static Operation * vectorizeAffineLoad(AffineLoadOp loadOp, VectorizationState &state)
Vectorizes an affine load with the vectorization strategy in 'state' by generating a 'vector....
static Value createMask(AffineForOp vecForOp, VectorizationState &state)
Creates a mask used to filter out garbage elements in the last iteration of unaligned loops.
static std::optional< NestedPattern > makePattern(const DenseSet< Operation * > &parallelLoops, int vectorRank, ArrayRef< int64_t > fastestVaryingPattern)
Creates a vectorization pattern from the command line arguments.
static AffineMap makePermutationMap(ArrayRef< Value > indices, const DenseMap< Operation *, unsigned > &enclosingLoopToVectorDim)
Constructs a permutation map from memref indices to vector dimension.
Base type for affine expression.
Definition: AffineExpr.h:69
A multi-dimensional affine map Affine map's are immutable like Type's, and they are uniqued.
Definition: AffineMap.h:47
static AffineMap get(MLIRContext *context)
Returns a zero result affine map with no dimensions or symbols: () -> ().
unsigned getNumSymbols() const
Definition: AffineMap.cpp:386
unsigned getNumDims() const
Definition: AffineMap.cpp:382
ArrayRef< AffineExpr > getResults() const
Definition: AffineMap.cpp:395
unsigned getNumResults() const
Definition: AffineMap.cpp:390
Attributes are known-constant values of operations.
Definition: Attributes.h:25
This class represents an argument of a Block.
Definition: Value.h:319
static DenseElementsAttr get(ShapedType type, ArrayRef< Attribute > values)
Constructs a dense elements attribute from an array of element values.
This is a utility class for mapping one set of IR entities to another.
Definition: IRMapping.h:26
This class defines the main interface for locations in MLIR and acts as a non-nullable wrapper around...
Definition: Location.h:63
MLIRContext is the top-level object for a collection of MLIR operations.
Definition: MLIRContext.h:60
RAII guard to reset the insertion point of the builder when destroyed.
Definition: Builders.h:350
This class helps build Operations.
Definition: Builders.h:209
StringAttr getIdentifier() const
Return the name of this operation as a StringAttr.
Operation is the basic unit of execution within MLIR.
Definition: Operation.h:88
OpResult getResult(unsigned idx)
Get the 'idx'th result of this operation.
Definition: Operation.h:402
unsigned getNumRegions()
Returns the number of regions held by this operation.
Definition: Operation.h:669
Location getLoc()
The source location the operation was defined or derived from.
Definition: Operation.h:223
unsigned getNumOperands()
Definition: Operation.h:341
Operation * getParentOp()
Returns the closest surrounding operation that contains this operation or nullptr if this is a top-le...
Definition: Operation.h:234
ArrayRef< NamedAttribute > getAttrs()
Return all of the attributes on this operation.
Definition: Operation.h:507
OperationName getName()
The name of an operation is the key identifier for it.
Definition: Operation.h:119
operand_range getOperands()
Returns an iterator on the underlying Value's.
Definition: Operation.h:373
result_range getResults()
Definition: Operation.h:410
void erase()
Remove this operation from its parent block and delete it.
Definition: Operation.cpp:539
unsigned getNumResults()
Return the number of results held by this operation.
Definition: Operation.h:399
Instances of the Type class are uniqued, have an immutable identifier and an optional mutable compone...
Definition: Types.h:74
This class provides an abstraction over the different types of ranges over Values.
Definition: ValueRange.h:381
This class represents an instance of an SSA value in the MLIR system, representing a computable value...
Definition: Value.h:96
bool use_empty() const
Returns true if this value has no uses.
Definition: Value.h:218
Type getType() const
Return the type of this value.
Definition: Value.h:129
Location getLoc() const
Return the location of this value.
Definition: Value.cpp:26
Operation * getDefiningOp() const
If this value is the result of an operation, return the operation that defines it.
Definition: Value.cpp:20
static WalkResult advance()
Definition: Visitors.h:52
static WalkResult interrupt()
Definition: Visitors.h:51
An NestedPattern captures nested patterns in the IR.
Definition: NestedMatcher.h:47
Operation * getMatchedOperation() const
Definition: NestedMatcher.h:56
ArrayRef< NestedMatch > getMatchedChildren()
Definition: NestedMatcher.h:57
RAII structure to transparently manage the bump allocator for NestedPattern and NestedMatch classes.
NestedPattern For(const NestedPattern &child)
NestedPattern Op(FilterFunctionType filter=defaultFilterFunction)
bool isVectorizableLoopBody(AffineForOp loop, NestedPattern &vectorTransferMatcher)
Checks whether the loop is structurally vectorizable; i.e.
AffineForOp getForInductionVarOwner(Value val)
Returns the loop parent of an induction variable.
Definition: AffineOps.cpp:2563
AffineApplyOp makeComposedAffineApply(OpBuilder &b, Location loc, AffineMap map, ArrayRef< OpFoldResult > operands)
Returns a composed AffineApplyOp by composing map and operands with other AffineApplyOps supplying th...
Definition: AffineOps.cpp:1142
std::function< bool(Operation &)> FilterFunctionType
A NestedPattern is a nested operation walker that:
Definition: NestedMatcher.h:91
void vectorizeAffineLoops(Operation *parentOp, llvm::DenseSet< Operation *, DenseMapInfo< Operation * >> &loops, ArrayRef< int64_t > vectorSizes, ArrayRef< int64_t > fastestVaryingPattern, const ReductionLoopMap &reductionLoops=ReductionLoopMap())
Vectorizes affine loops in 'loops' using the n-D vectorization factors in 'vectorSizes'.
bool isLoopParallel(AffineForOp forOp, SmallVectorImpl< LoopReduction > *parallelReductions=nullptr)
Returns true if ‘forOp’ is a parallel loop.
LogicalResult vectorizeAffineLoopNest(std::vector< SmallVector< AffineForOp, 2 >> &loops, const VectorizationStrategy &strategy)
External utility to vectorize affine loops from a single loop nest using an n-D vectorization strateg...
TypedAttr getIdentityValueAttr(AtomicRMWKind kind, Type resultType, OpBuilder &builder, Location loc, bool useOnlyFiniteValue=false)
Returns the identity value attribute associated with an AtomicRMWKind op.
Definition: ArithOps.cpp:2474
Value getReductionOp(AtomicRMWKind op, OpBuilder &builder, Location loc, Value lhs, Value rhs)
Returns the value obtained by applying the reduction operation kind associated with a binary AtomicRM...
Definition: ArithOps.cpp:2593
Value getVectorReductionOp(arith::AtomicRMWKind op, OpBuilder &builder, Location loc, Value vector)
Returns the value obtained by reducing the vector into a scalar using the operation kind associated w...
Definition: VectorOps.cpp:609
Include the generated interface declarations.
LogicalResult failure(bool isFailure=true)
Utility function to generate a LogicalResult.
Definition: LogicalResult.h:62
bool succeeded(LogicalResult result)
Utility function that returns true if the provided LogicalResult corresponds to a success value.
Definition: LogicalResult.h:68
LogicalResult success(bool isSuccess=true)
Utility function to generate a LogicalResult.
Definition: LogicalResult.h:56
Value matchReduction(ArrayRef< BlockArgument > iterCarriedArgs, unsigned redPos, SmallVectorImpl< Operation * > &combinerOps)
Utility to match a generic reduction given a list of iteration-carried arguments, iterCarriedArgs and...
auto get(MLIRContext *context, Ts &&...params)
Helper method that injects context only if needed, this helps unify some of the attribute constructio...
bool failed(LogicalResult result)
Utility function that returns true if the provided LogicalResult corresponds to a failure value.
Definition: LogicalResult.h:72
Contains the vectorization state and related methods used across the vectorization process of a given...
This class represents an efficient way to signal success or failure.
Definition: LogicalResult.h:26
Holds parameters to perform n-D vectorization on a single loop nest.
Definition: Utils.h:93
SmallVector< int64_t, 8 > vectorSizes
Definition: Utils.h:96
DenseMap< Operation *, unsigned > loopToVectorDim
Definition: Utils.h:100
ReductionLoopMap reductionLoops
Definition: Utils.h:103