MLIR  16.0.0git
SCFToGPU.cpp
Go to the documentation of this file.
1 //===- SCFToGPU.cpp - Convert an affine loop nest to a GPU kernel -------===//
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 implements a straightforward conversion of an loop nest into a GPU
10 // kernel. The caller is expected to guarantee that the conversion is correct
11 // or to further transform the kernel to ensure correctness.
12 //
13 //===----------------------------------------------------------------------===//
14 
16 
24 #include "mlir/IR/AffineExpr.h"
26 #include "mlir/IR/Builders.h"
27 #include "mlir/Pass/Pass.h"
29 #include "mlir/Transforms/Passes.h"
31 #include "llvm/ADT/Sequence.h"
32 #include "llvm/Support/Debug.h"
33 
34 #define DEBUG_TYPE "loops-to-gpu"
35 
36 using namespace mlir;
37 using namespace mlir::scf;
38 
39 // Name of internal attribute to mark visited operations during conversion.
40 //
41 // NOTE: The conversion originally used the following legality criteria:
42 // `!parallelOp->hasAttr(gpu::getMappingAttrName())`
43 // But the provided pattern might reject some cases based on more detailed
44 // analysis of the `mapping` attribute.
45 // To avoid dialect conversion failure due to non-converted illegal operation
46 // we use this extra Unit attribute as a marker, that the operation was checked
47 // by the pattern and is should be considered as legal in the following legality
48 // checks. The `finalizeParallelLoopToGPUConversion` function performs clean up
49 // of this extra attributes ans is supposed to be called after the dialect
50 // conversion.
51 //
52 // TODO: Implement a cleaner solution, factoring out the "matching" logic
53 // from the pattern and its callees into a separate function that can be called
54 // from both the pattern and the op legality check.
55 static constexpr StringLiteral kVisitedAttrName = "SCFToGPU_visited";
56 
57 // Extract an indexed value from KernelDim3.
58 static Value getDim3Value(const gpu::KernelDim3 &dim3, unsigned pos) {
59  switch (pos) {
60  case 0:
61  return dim3.x;
62  case 1:
63  return dim3.y;
64  case 2:
65  return dim3.z;
66  default:
67  llvm_unreachable("dim3 position out of bounds");
68  }
69  return nullptr;
70 }
71 
72 // Get the lower bound-related operands of a loop operation.
74  return forOp.getLowerBoundOperands();
75 }
76 
77 // Get the upper bound-related operands of a loop operation.
79  return forOp.getUpperBoundOperands();
80 }
81 
82 // Get a Value that corresponds to the loop step. If the step is an attribute,
83 // materialize a corresponding constant using builder.
84 static Value getOrCreateStep(AffineForOp forOp, OpBuilder &builder) {
85  return builder.create<arith::ConstantIndexOp>(forOp.getLoc(),
86  forOp.getStep());
87 }
88 
89 // Get a Value for the loop lower bound. If the value requires computation,
90 // materialize the instructions using builder.
91 static Value getOrEmitLowerBound(AffineForOp forOp, OpBuilder &builder) {
92  return lowerAffineLowerBound(forOp, builder);
93 }
94 
95 // Get a Value for the loop upper bound. If the value requires computation,
96 // materialize the instructions using builder.
97 static Value getOrEmitUpperBound(AffineForOp forOp, OpBuilder &builder) {
98  return lowerAffineUpperBound(forOp, builder);
99 }
100 
101 // Check the structure of the loop nest:
102 // - there are enough loops to map to numDims;
103 // - the loops are perfectly nested;
104 // - the loop bounds can be computed above the outermost loop.
105 // This roughly corresponds to the "matcher" part of the pattern-based
106 // rewriting infrastructure.
108  unsigned numDims) {
109  Region &limit = forOp.getRegion();
110  for (unsigned i = 0, e = numDims; i < e; ++i) {
111  Operation *nested = &forOp.getBody()->front();
112  if (!areValuesDefinedAbove(getLowerBoundOperands(forOp), limit) ||
114  return forOp.emitError(
115  "loops with bounds depending on other mapped loops "
116  "are not supported");
117 
118  // The innermost loop can have an arbitrary body, skip the perfect nesting
119  // check for it.
120  if (i == e - 1)
121  break;
122 
123  auto begin = forOp.getBody()->begin(), end = forOp.getBody()->end();
124  if (forOp.getBody()->empty() || std::next(begin, 2) != end)
125  return forOp.emitError("expected perfectly nested loops in the body");
126 
127  if (!(forOp = dyn_cast<AffineForOp>(nested)))
128  return nested->emitError("expected a nested loop");
129  }
130  return success();
131 }
132 
134  unsigned numBlockDims,
135  unsigned numThreadDims) {
136  if (numBlockDims < 1 || numThreadDims < 1) {
137  LLVM_DEBUG(llvm::dbgs() << "nothing to map");
138  return success();
139  }
140 
141  if (numBlockDims > 3) {
142  return forOp.emitError("cannot map to more than 3 block dimensions");
143  }
144  if (numThreadDims > 3) {
145  return forOp.emitError("cannot map to more than 3 thread dimensions");
146  }
147  return checkAffineLoopNestMappableImpl(forOp, numBlockDims + numThreadDims);
148 }
149 
150 namespace {
151 // Helper structure that holds common state of the loop to GPU kernel
152 // conversion.
153 struct AffineLoopToGpuConverter {
154  Optional<AffineForOp> collectBounds(AffineForOp forOp, unsigned numLoops);
155 
156  void createLaunch(AffineForOp rootForOp, AffineForOp innermostForOp,
157  unsigned numBlockDims, unsigned numThreadDims);
158 
159  // Ranges of the loops mapped to blocks or threads.
161  // Lower bounds of the loops mapped to blocks or threads.
163  // Induction variables of the loops mapped to blocks or threads.
165  // Steps of the loops mapped to blocks or threads.
166  SmallVector<Value, 6> steps;
167 };
168 } // namespace
169 
170 // Return true if the value is obviously a constant "one".
171 static bool isConstantOne(Value value) {
172  if (auto def = value.getDefiningOp<arith::ConstantIndexOp>())
173  return def.value() == 1;
174  return false;
175 }
176 
177 // Collect ranges, bounds, steps and induction variables in preparation for
178 // mapping a loop nest of depth "numLoops" rooted at "forOp" to a GPU kernel.
179 // This may fail if the IR for computing loop bounds cannot be constructed, for
180 // example if an affine loop uses semi-affine maps. Return the last loop to be
181 // mapped on success, llvm::None on failure.
183 AffineLoopToGpuConverter::collectBounds(AffineForOp forOp, unsigned numLoops) {
184  OpBuilder builder(forOp.getOperation());
185  dims.reserve(numLoops);
186  lbs.reserve(numLoops);
187  ivs.reserve(numLoops);
188  steps.reserve(numLoops);
189  AffineForOp currentLoop = forOp;
190  for (unsigned i = 0; i < numLoops; ++i) {
191  Value lowerBound = getOrEmitLowerBound(currentLoop, builder);
192  Value upperBound = getOrEmitUpperBound(currentLoop, builder);
193  if (!lowerBound || !upperBound) {
194  return llvm::None;
195  }
196 
197  Value range = builder.create<arith::SubIOp>(currentLoop.getLoc(),
198  upperBound, lowerBound);
199  Value step = getOrCreateStep(currentLoop, builder);
200  if (!isConstantOne(step))
201  range = builder.create<arith::DivSIOp>(currentLoop.getLoc(), range, step);
202  dims.push_back(range);
203 
204  lbs.push_back(lowerBound);
205  ivs.push_back(currentLoop.getInductionVar());
206  steps.push_back(step);
207 
208  if (i != numLoops - 1)
209  currentLoop = cast<AffineForOp>(&currentLoop.getBody()->front());
210  }
211  return currentLoop;
212 }
213 
214 // Replace the rooted at "rootForOp" with a GPU launch operation. This expects
215 // "innermostForOp" to point to the last loop to be transformed to the kernel,
216 // and to have (numBlockDims + numThreadDims) perfectly nested loops between
217 // "rootForOp" and "innermostForOp".
218 void AffineLoopToGpuConverter::createLaunch(AffineForOp rootForOp,
219  AffineForOp innermostForOp,
220  unsigned numBlockDims,
221  unsigned numThreadDims) {
222  OpBuilder builder(rootForOp.getOperation());
223  // Prepare the grid and block sizes for the launch operation. If there is
224  // no loop mapped to a specific dimension, use constant "1" as its size.
225  Value constOne =
226  (numBlockDims < 3 || numThreadDims < 3)
227  ? builder.create<arith::ConstantIndexOp>(rootForOp.getLoc(), 1)
228  : nullptr;
229  Value gridSizeX = numBlockDims > 0 ? dims[0] : constOne;
230  Value gridSizeY = numBlockDims > 1 ? dims[1] : constOne;
231  Value gridSizeZ = numBlockDims > 2 ? dims[2] : constOne;
232  Value blockSizeX = numThreadDims > 0 ? dims[numBlockDims] : constOne;
233  Value blockSizeY = numThreadDims > 1 ? dims[numBlockDims + 1] : constOne;
234  Value blockSizeZ = numThreadDims > 2 ? dims[numBlockDims + 2] : constOne;
235 
236  // Create a launch op and move the body region of the innermost loop to the
237  // launch op.
238  auto launchOp = builder.create<gpu::LaunchOp>(
239  rootForOp.getLoc(), gridSizeX, gridSizeY, gridSizeZ, blockSizeX,
240  blockSizeY, blockSizeZ);
241 
242  // Replace the loop terminator (loops contain only a single block) with the
243  // gpu terminator and move the operations from the loop body block to the gpu
244  // launch body block. Do not move the entire block because of the difference
245  // in block arguments.
246  Operation &terminator = innermostForOp.getBody()->back();
247  Location terminatorLoc = terminator.getLoc();
248  terminator.erase();
249  builder.setInsertionPointToEnd(innermostForOp.getBody());
250  builder.create<gpu::TerminatorOp>(terminatorLoc, llvm::None);
251  launchOp.body().front().getOperations().splice(
252  launchOp.body().front().begin(),
253  innermostForOp.getBody()->getOperations());
254 
255  // Remap the loop iterators to use block/thread identifiers instead. Loops
256  // may iterate from LB with step S whereas GPU thread/block ids always iterate
257  // from 0 to N with step 1. Therefore, loop induction variables are replaced
258  // with (gpu-thread/block-id * S) + LB.
259  builder.setInsertionPointToStart(&launchOp.body().front());
260  auto *lbArgumentIt = lbs.begin();
261  auto *stepArgumentIt = steps.begin();
262  for (const auto &en : llvm::enumerate(ivs)) {
263  Value id =
264  en.index() < numBlockDims
265  ? getDim3Value(launchOp.getBlockIds(), en.index())
266  : getDim3Value(launchOp.getThreadIds(), en.index() - numBlockDims);
267  Value step = steps[en.index()];
268  if (!isConstantOne(step))
269  id = builder.create<arith::MulIOp>(rootForOp.getLoc(), step, id);
270 
271  Value ivReplacement =
272  builder.create<arith::AddIOp>(rootForOp.getLoc(), *lbArgumentIt, id);
273  en.value().replaceAllUsesWith(ivReplacement);
274  std::advance(lbArgumentIt, 1);
275  std::advance(stepArgumentIt, 1);
276  }
277 
278  // We are done and can erase the original outermost loop.
279  rootForOp.erase();
280 }
281 
282 // Generic loop to GPU kernel conversion function.
284  unsigned numBlockDims,
285  unsigned numThreadDims) {
286  if (failed(checkAffineLoopNestMappable(forOp, numBlockDims, numThreadDims)))
287  return failure();
288 
289  AffineLoopToGpuConverter converter;
290  auto maybeInnerLoop =
291  converter.collectBounds(forOp, numBlockDims + numThreadDims);
292  if (!maybeInnerLoop)
293  return failure();
294  converter.createLaunch(forOp, *maybeInnerLoop, numBlockDims, numThreadDims);
295 
296  return success();
297 }
298 
300  unsigned numBlockDims,
301  unsigned numThreadDims) {
302  return ::convertAffineLoopNestToGPULaunch(forOp, numBlockDims, numThreadDims);
303 }
304 
305 namespace {
306 struct ParallelToGpuLaunchLowering : public OpRewritePattern<ParallelOp> {
308 
309  LogicalResult matchAndRewrite(ParallelOp parallelOp,
310  PatternRewriter &rewriter) const override;
311 };
312 } // namespace
313 
314 /// Tries to derive a static upper bound from the defining operation of
315 /// `upperBound`.
317  PatternRewriter &rewriter) {
318  if (auto op = upperBound.getDefiningOp<arith::ConstantIndexOp>()) {
319  return op;
320  }
321 
322  if (auto minOp = upperBound.getDefiningOp<AffineMinOp>()) {
323  for (const AffineExpr &result : minOp.getMap().getResults()) {
324  if (auto constExpr = result.dyn_cast<AffineConstantExpr>()) {
325  return rewriter.create<arith::ConstantIndexOp>(minOp.getLoc(),
326  constExpr.getValue());
327  }
328  }
329  }
330 
331  if (auto multiplyOp = upperBound.getDefiningOp<arith::MulIOp>()) {
332  if (auto lhs = dyn_cast_or_null<arith::ConstantIndexOp>(
333  deriveStaticUpperBound(multiplyOp.getOperand(0), rewriter)
334  .getDefiningOp()))
335  if (auto rhs = dyn_cast_or_null<arith::ConstantIndexOp>(
336  deriveStaticUpperBound(multiplyOp.getOperand(1), rewriter)
337  .getDefiningOp())) {
338  // Assumptions about the upper bound of minimum computations no longer
339  // work if multiplied by a negative value, so abort in this case.
340  if (lhs.value() < 0 || rhs.value() < 0)
341  return {};
342 
343  return rewriter.create<arith::ConstantIndexOp>(
344  multiplyOp.getLoc(), lhs.value() * rhs.value());
345  }
346  }
347 
348  return {};
349 }
350 
351 static bool isMappedToProcessor(gpu::Processor processor) {
352  return processor != gpu::Processor::Sequential;
353 }
354 
355 static unsigned getLaunchOpArgumentNum(gpu::Processor processor) {
356  switch (processor) {
357  case gpu::Processor::BlockX:
358  return 0;
359  case gpu::Processor::BlockY:
360  return 1;
361  case gpu::Processor::BlockZ:
362  return 2;
363  case gpu::Processor::ThreadX:
364  return 3;
365  case gpu::Processor::ThreadY:
366  return 4;
367  case gpu::Processor::ThreadZ:
368  return 5;
369  default:;
370  }
371  llvm_unreachable(
372  "invalid processor type while retrieving launch op argument number");
373 }
374 
375 /// Modifies the current transformation state to capture the effect of the given
376 /// `scf.parallel` operation on index substitutions and the operations to be
377 /// inserted.
378 /// Specifically, if a dimension of a parallel loop is mapped to a hardware id,
379 /// this function will
380 /// - compute the loop index based on the hardware id and affine map from the
381 /// mapping and update `cloningMap` to substitute all uses.
382 /// - derive a new upper bound for the hardware id and augment the provided
383 /// `gpu.launch operation` accordingly.
384 /// - if the upper bound is imprecise, insert a conditional in the `gpu.launch`
385 /// and update the rewriter to insert into the conditional's body.
386 /// If the dimension is mapped to sequential,
387 /// - insert a for loop into the body and update the rewriter to insert into
388 /// the for loop's body.
389 /// - update the `cloningMap` to replace uses of the index with the index of
390 /// the new for loop.
391 /// In either case,
392 /// - append the instructions from the loops body to worklist, in reverse order.
393 /// To note the end of the current scope in case a loop or conditional was
394 /// inserted, a sentinel (the `gpu.launch` operation) is inserted into the
395 /// worklist. This signals the processor of the worklist to pop the rewriter
396 /// one scope-level up.
398  ParallelOp parallelOp, gpu::LaunchOp launchOp,
399  BlockAndValueMapping &cloningMap, SmallVectorImpl<Operation *> &worklist,
401  // TODO: Verify that this is a valid GPU mapping.
402  // processor ids: 0-2 block [x/y/z], 3-5 -> thread [x/y/z], 6-> sequential
403  ArrayAttr mapping =
404  parallelOp->getAttrOfType<ArrayAttr>(gpu::getMappingAttrName());
405 
406  // TODO: Support reductions.
407  if (!mapping || parallelOp.getNumResults() != 0)
408  return failure();
409 
410  Location loc = parallelOp.getLoc();
411 
412  auto launchIndependent = [&launchOp](Value val) {
413  return val.getParentRegion()->isAncestor(launchOp->getParentRegion());
414  };
415 
416  auto ensureLaunchIndependent = [&rewriter,
417  launchIndependent](Value val) -> Value {
418  if (launchIndependent(val))
419  return val;
420  if (auto constOp = val.getDefiningOp<arith::ConstantOp>())
421  return rewriter.create<arith::ConstantOp>(constOp.getLoc(),
422  constOp.getValue());
423  return {};
424  };
425 
426  for (auto config : llvm::zip(
427  mapping, parallelOp.getInductionVars(), parallelOp.getLowerBound(),
428  parallelOp.getUpperBound(), parallelOp.getStep())) {
429  Attribute mappingAttribute;
430  Value iv, lowerBound, upperBound, step;
431  std::tie(mappingAttribute, iv, lowerBound, upperBound, step) = config;
432  auto annotation =
433  mappingAttribute.dyn_cast<gpu::ParallelLoopDimMappingAttr>();
434  if (!annotation)
435  return parallelOp.emitOpError()
436  << "expected mapping attribute for lowering to GPU";
437  Value newIndex;
438  gpu::Processor processor = annotation.getProcessor();
439 
440  if (isMappedToProcessor(processor)) {
441  // Use the corresponding thread/grid index as replacement for the loop iv.
442  Value operand =
443  launchOp.body().getArgument(getLaunchOpArgumentNum(processor));
444  // Take the indexmap and add the lower bound and step computations in.
445  // This computes operand * step + lowerBound.
446  // Use an affine map here so that it composes nicely with the provided
447  // annotation.
448  AffineMap lowerAndStep = AffineMap::get(
449  1, 2,
450  rewriter.getAffineDimExpr(0) * rewriter.getAffineSymbolExpr(0) +
451  rewriter.getAffineSymbolExpr(1));
452  newIndex = rewriter.create<AffineApplyOp>(
453  loc, annotation.getMap().compose(lowerAndStep),
454  ValueRange{operand, step, lowerBound});
455  // If there was also a bound, insert that, too.
456  // TODO: Check that we do not assign bounds twice.
457  if (annotation.getBound()) {
458  // We pass as the single operand to the bound-map the number of
459  // iterations, which is (upperBound - lowerBound) ceilDiv step. To
460  // support inner loops with dynamic upper bounds (as generated by e.g.
461  // tiling), try to derive a max for the bounds. If the used bound for
462  // the hardware id is imprecise, wrap the contained code into a
463  // conditional. If the lower-bound is constant or defined before the
464  // launch, we can use it in the launch bounds. Otherwise fail.
465  if (!launchIndependent(lowerBound) &&
466  !isa_and_nonnull<arith::ConstantOp>(lowerBound.getDefiningOp()))
467  return failure();
468  // The step must also be constant or defined outside of the loop nest.
469  if (!launchIndependent(step) &&
470  !isa_and_nonnull<arith::ConstantOp>(step.getDefiningOp()))
471  return failure();
472  // If the upper-bound is constant or defined before the launch, we can
473  // use it in the launch bounds directly. Otherwise try derive a bound.
474  bool boundIsPrecise =
475  launchIndependent(upperBound) ||
476  isa_and_nonnull<arith::ConstantOp>(upperBound.getDefiningOp());
477  {
478  PatternRewriter::InsertionGuard guard(rewriter);
479  rewriter.setInsertionPoint(launchOp);
480  if (!boundIsPrecise) {
481  upperBound = deriveStaticUpperBound(upperBound, rewriter);
482  if (!upperBound) {
483  return rewriter.notifyMatchFailure(
484  parallelOp,
485  "cannot derive loop-invariant upper bound for number of"
486  "iterations");
487  }
488  }
489  // Compute the number of iterations needed. We compute this as an
490  // affine expression ceilDiv (upperBound - lowerBound) step. We use
491  // affine.apply here so that it composes nicely with the provided map.
492  AffineMap stepMap = AffineMap::get(
493  1, 2,
494  ((rewriter.getAffineDimExpr(0) - rewriter.getAffineSymbolExpr(0))
495  .ceilDiv(rewriter.getAffineSymbolExpr(1))));
496  Value launchBound = rewriter.create<AffineApplyOp>(
497  loc, annotation.getBound().compose(stepMap),
498  ValueRange{
499  ensureLaunchIndependent(
500  cloningMap.lookupOrDefault(upperBound)),
501  ensureLaunchIndependent(
502  cloningMap.lookupOrDefault(lowerBound)),
503  ensureLaunchIndependent(cloningMap.lookupOrDefault(step))});
504  // todo(herhut,ravishankarm): Update the behavior of setMappingAttr
505  // when this condition is relaxed.
506  if (bounds.find(processor) != bounds.end()) {
507  return rewriter.notifyMatchFailure(
508  parallelOp, "cannot redefine the bound for processor " +
509  Twine(static_cast<int64_t>(processor)));
510  }
511  bounds[processor] = launchBound;
512  }
513  if (!boundIsPrecise) {
514  // We are using an approximation, create a surrounding conditional.
515  Value originalBound = std::get<3>(config);
516  arith::CmpIOp pred = rewriter.create<arith::CmpIOp>(
517  loc, arith::CmpIPredicate::slt, newIndex,
518  cloningMap.lookupOrDefault(originalBound));
519  scf::IfOp ifOp = rewriter.create<scf::IfOp>(loc, pred, false);
520  rewriter.setInsertionPointToStart(&ifOp.getThenRegion().front());
521  // Put a sentinel into the worklist so we know when to pop out of the
522  // if body again. We use the launchOp here, as that cannot be part of
523  // the bodies instruction.
524  worklist.push_back(launchOp.getOperation());
525  }
526  }
527  } else {
528  // Create a sequential for loop.
529  auto loopOp = rewriter.create<scf::ForOp>(
530  loc, cloningMap.lookupOrDefault(lowerBound),
531  cloningMap.lookupOrDefault(upperBound),
532  cloningMap.lookupOrDefault(step));
533  newIndex = loopOp.getInductionVar();
534  rewriter.setInsertionPointToStart(loopOp.getBody());
535  // Put a sentinel into the worklist so we know when to pop out of the loop
536  // body again. We use the launchOp here, as that cannot be part of the
537  // bodies instruction.
538  worklist.push_back(launchOp.getOperation());
539  }
540  cloningMap.map(iv, newIndex);
541  }
542 
543  // Propagate custom user defined optional attributes, that can be used at
544  // later stage, such as extension data for GPU kernel dispatch
545  for (const auto &namedAttr : parallelOp->getAttrs()) {
546  if (namedAttr.getName() == gpu::getMappingAttrName() ||
547  namedAttr.getName() == ParallelOp::getOperandSegmentSizeAttr())
548  continue;
549  launchOp->setAttr(namedAttr.getName(), namedAttr.getValue());
550  }
551 
552  Block *body = parallelOp.getBody();
553  worklist.reserve(worklist.size() + body->getOperations().size());
554  for (Operation &op : llvm::reverse(body->without_terminator()))
555  worklist.push_back(&op);
556  return success();
557 }
558 
559 /// Lower a `scf.parallel` operation into a corresponding `gpu.launch`
560 /// operation.
561 ///
562 /// This essentially transforms a loop nest into a corresponding SIMT function.
563 /// The conversion is driven by mapping annotations on the `scf.parallel`
564 /// operations. The mapping is provided via a `DictionaryAttribute` named
565 /// `mapping`, which has three entries:
566 /// - processor: the hardware id to map to. 0-2 are block dimensions, 3-5 are
567 /// thread dimensions and 6 is sequential.
568 /// - map : An affine map that is used to pre-process hardware ids before
569 /// substitution.
570 /// - bound : An affine map that is used to compute the bound of the hardware
571 /// id based on an upper bound of the number of iterations.
572 /// If the `scf.parallel` contains nested `scf.parallel` operations, those
573 /// need to be annotated, as well. Structurally, the transformation works by
574 /// splicing all operations from nested `scf.parallel` operations into a single
575 /// sequence. Indices mapped to hardware ids are substituted with those ids,
576 /// wheras sequential mappings result in a sequential for-loop. To have more
577 /// flexibility when mapping code to hardware ids, the transform supports two
578 /// affine maps. The first `map` is used to compute the actual index for
579 /// substitution from the hardware id. The second `bound` is used to compute the
580 /// launch dimension for the hardware id from the number of iterations the
581 /// mapped loop is performing. Note that the number of iterations might be
582 /// imprecise if the corresponding loop-bounds are loop-dependent. In such case,
583 /// the hardware id might iterate over additional indices. The transformation
584 /// caters for this by predicating the created sequence of instructions on
585 /// the actual loop bound. This only works if an static upper bound for the
586 /// dynamic loop bound can be derived, currently via analyzing `affine.min`
587 /// operations.
589 ParallelToGpuLaunchLowering::matchAndRewrite(ParallelOp parallelOp,
590  PatternRewriter &rewriter) const {
591  // Mark the operation as visited for recursive legality check.
592  parallelOp->setAttr(kVisitedAttrName, rewriter.getUnitAttr());
593 
594  // We can only transform starting at the outer-most loop. Launches inside of
595  // parallel loops are not supported.
596  if (auto parentLoop = parallelOp->getParentOfType<ParallelOp>())
597  return failure();
598  // Create a launch operation. We start with bound one for all grid/block
599  // sizes. Those will be refined later as we discover them from mappings.
600  Location loc = parallelOp.getLoc();
602  rewriter.create<arith::ConstantIndexOp>(parallelOp.getLoc(), 1);
603  gpu::LaunchOp launchOp = rewriter.create<gpu::LaunchOp>(
604  parallelOp.getLoc(), constantOne, constantOne, constantOne, constantOne,
606  rewriter.setInsertionPointToEnd(&launchOp.body().front());
607  rewriter.create<gpu::TerminatorOp>(loc);
608  rewriter.setInsertionPointToStart(&launchOp.body().front());
609 
610  BlockAndValueMapping cloningMap;
613  if (failed(processParallelLoop(parallelOp, launchOp, cloningMap, worklist,
614  launchBounds, rewriter)))
615  return failure();
616 
617  // Whether we have seen any side-effects. Reset when leaving an inner scope.
618  bool seenSideeffects = false;
619  // Whether we have left a nesting scope (and hence are no longer innermost).
620  bool leftNestingScope = false;
621  while (!worklist.empty()) {
622  Operation *op = worklist.pop_back_val();
623  // Now walk over the body and clone it.
624  // TODO: This is only correct if there either is no further scf.parallel
625  // nested or this code is side-effect free. Otherwise we might need
626  // predication. We are overly conservative for now and only allow
627  // side-effects in the innermost scope.
628  if (auto nestedParallel = dyn_cast<ParallelOp>(op)) {
629  // Before entering a nested scope, make sure there have been no
630  // sideeffects until now.
631  if (seenSideeffects)
632  return failure();
633  // A nested scf.parallel needs insertion of code to compute indices.
634  // Insert that now. This will also update the worklist with the loops
635  // body.
636  if (failed(processParallelLoop(nestedParallel, launchOp, cloningMap,
637  worklist, launchBounds, rewriter)))
638  return failure();
639  } else if (op == launchOp.getOperation()) {
640  // Found our sentinel value. We have finished the operations from one
641  // nesting level, pop one level back up.
642  auto *parent = rewriter.getInsertionPoint()->getParentOp();
643  rewriter.setInsertionPointAfter(parent);
644  leftNestingScope = true;
645  seenSideeffects = false;
646  } else {
647  // Otherwise we copy it over.
648  Operation *clone = rewriter.clone(*op, cloningMap);
649  cloningMap.map(op->getResults(), clone->getResults());
650  // Check for side effects.
651  // TODO: Handle region side effects properly.
652  seenSideeffects |= !MemoryEffectOpInterface::hasNoEffect(clone) ||
653  clone->getNumRegions() != 0;
654  // If we are no longer in the innermost scope, sideeffects are disallowed.
655  if (seenSideeffects && leftNestingScope)
656  return failure();
657  }
658  }
659 
660  // Now that we succeeded creating the launch operation, also update the
661  // bounds.
662  for (auto bound : launchBounds)
663  launchOp.setOperand(getLaunchOpArgumentNum(std::get<0>(bound)),
664  std::get<1>(bound));
665 
666  rewriter.eraseOp(parallelOp);
667  return success();
668 }
669 
671  patterns.add<ParallelToGpuLaunchLowering>(patterns.getContext());
672 }
673 
675  target.addLegalDialect<memref::MemRefDialect>();
676  target.addDynamicallyLegalOp<scf::ParallelOp>([](scf::ParallelOp parallelOp) {
677  return !parallelOp->hasAttr(gpu::getMappingAttrName()) ||
678  parallelOp->hasAttr(kVisitedAttrName);
679  });
680 }
681 
683  op->walk([](scf::ParallelOp parallelOp) {
684  parallelOp->removeAttr(kVisitedAttrName);
685  });
686 }
static Value getOrEmitUpperBound(AffineForOp forOp, OpBuilder &builder)
Definition: SCFToGPU.cpp:97
Include the generated interface declarations.
This class contains a list of basic blocks and a link to the parent operation it is attached to...
Definition: Region.h:26
RewritePatternSet & add(ConstructorArg &&arg, ConstructorArgs &&...args)
Add an instance of each of the pattern types &#39;Ts&#39; to the pattern list with the given arguments...
bool areValuesDefinedAbove(Range values, Region &limit)
Check if all values in the provided range are defined above the limit region.
Definition: RegionUtils.h:24
A special type of RewriterBase that coordinates the application of a rewrite pattern on the current I...
Definition: PatternMatch.h:600
Operation is a basic unit of execution within MLIR.
Definition: Operation.h:28
static unsigned getLaunchOpArgumentNum(gpu::Processor processor)
Definition: SCFToGPU.cpp:355
unsigned getNumRegions()
Returns the number of regions held by this operation.
Definition: Operation.h:477
Block represents an ordered list of Operations.
Definition: Block.h:29
StringRef getMappingAttrName()
Name of the mapping attribute produced by loop mappers.
void setInsertionPoint(Block *block, Block::iterator insertPoint)
Set the insertion point to the specified location.
Definition: Builders.h:344
virtual void eraseOp(Operation *op)
This method erases an operation that is known to have no uses.
Operation * clone(Operation &op, BlockAndValueMapping &mapper)
Creates a deep copy of the specified operation, remapping any operands that use values outside of the...
Definition: Builders.cpp:492
static Operation::operand_range getUpperBoundOperands(AffineForOp forOp)
Definition: SCFToGPU.cpp:78
OpListType & getOperations()
Definition: Block.h:128
bool failed(LogicalResult result)
Utility function that returns true if the provided LogicalResult corresponds to a failure value...
Definition: LogicalResult.h:72
AffineExpr getAffineSymbolExpr(unsigned position)
Definition: Builders.cpp:313
static Value deriveStaticUpperBound(Value upperBound, PatternRewriter &rewriter)
Tries to derive a static upper bound from the defining operation of upperBound.
Definition: SCFToGPU.cpp:316
static Value getOrCreateStep(AffineForOp forOp, OpBuilder &builder)
Definition: SCFToGPU.cpp:84
void populateParallelLoopToGPUPatterns(RewritePatternSet &patterns)
Adds the conversion pattern from scf.parallel to gpu.launch to the provided pattern list...
Definition: SCFToGPU.cpp:670
static LogicalResult processParallelLoop(ParallelOp parallelOp, gpu::LaunchOp launchOp, BlockAndValueMapping &cloningMap, SmallVectorImpl< Operation *> &worklist, DenseMap< gpu::Processor, Value > &bounds, PatternRewriter &rewriter)
Modifies the current transformation state to capture the effect of the given scf.parallel operation o...
Definition: SCFToGPU.cpp:397
iterator_range< iterator > without_terminator()
Return an iterator range over the operation within this block excluding the terminator operation at t...
Definition: Block.h:200
An integer constant appearing in affine expression.
Definition: AffineExpr.h:232
void replaceAllUsesWith(Value newValue) const
Replace all uses of &#39;this&#39; value with the new value, updating anything in the IR that uses &#39;this&#39; to ...
Definition: Value.h:162
static constexpr const bool value
This class defines the main interface for locations in MLIR and acts as a non-nullable wrapper around...
Definition: Location.h:48
void finalizeParallelLoopToGPUConversion(Operation *op)
Clean up after applyPartialConversion/applyFullConversion call.
Definition: SCFToGPU.cpp:682
void map(Block *from, Block *to)
Inserts a new mapping for &#39;from&#39; to &#39;to&#39;.
void setInsertionPointAfter(Operation *op)
Sets the insertion point to the node after the specified operation, which will cause subsequent inser...
Definition: Builders.h:358
static bool isMappedToProcessor(gpu::Processor processor)
Definition: SCFToGPU.cpp:351
LogicalResult success(bool isSuccess=true)
Utility function to generate a LogicalResult.
Definition: LogicalResult.h:56
std::enable_if< llvm::function_traits< std::decay_t< FnT > >::num_args==1, RetT >::type walk(FnT &&callback)
Walk the operation by calling the callback for each nested operation (including this one)...
Definition: Operation.h:574
Operation * create(const OperationState &state)
Creates an operation given the fields represented as an OperationState.
Definition: Builders.cpp:404
This class represents an efficient way to signal success or failure.
Definition: LogicalResult.h:26
LogicalResult failure(bool isFailure=true)
Utility function to generate a LogicalResult.
Definition: LogicalResult.h:62
void addDynamicallyLegalOp(OperationName op, const DynamicLegalityCallbackFn &callback)
Register the given operation as dynamically legal and set the dynamic legalization callback to the on...
static LogicalResult convertAffineLoopNestToGPULaunch(AffineForOp forOp, unsigned numBlockDims, unsigned numThreadDims)
Definition: SCFToGPU.cpp:283
static AffineMap get(MLIRContext *context)
Returns a zero result affine map with no dimensions or symbols: () -> ().
static constexpr StringLiteral kVisitedAttrName
Definition: SCFToGPU.cpp:55
UnitAttr getUnitAttr()
Definition: Builders.cpp:85
Attributes are known-constant values of operations.
Definition: Attributes.h:24
int64_t ceilDiv(int64_t lhs, int64_t rhs)
Returns the result of MLIR&#39;s ceildiv operation on constants.
Definition: MathExtras.h:23
constexpr void enumerate(std::tuple< Tys... > &tuple, CallbackT &&callback)
Definition: Matchers.h:233
Base type for affine expression.
Definition: AffineExpr.h:68
static LogicalResult checkAffineLoopNestMappable(AffineForOp forOp, unsigned numBlockDims, unsigned numThreadDims)
Definition: SCFToGPU.cpp:133
Value lowerAffineLowerBound(AffineForOp op, OpBuilder &builder)
Emit code that computes the lower bound of the given affine loop using standard arithmetic operations...
A multi-dimensional affine map Affine map&#39;s are immutable like Type&#39;s, and they are uniqued...
Definition: AffineMap.h:42
static Operation::operand_range getLowerBoundOperands(AffineForOp forOp)
Definition: SCFToGPU.cpp:73
LogicalResult convertAffineLoopNestToGPULaunch(AffineForOp forOp, unsigned numBlockDims, unsigned numThreadDims)
Convert a perfect affine loop nest with the outermost loop identified by forOp into a gpu::Launch ope...
Definition: SCFToGPU.cpp:299
Utility class for the GPU dialect to represent triples of Values accessible through ...
Definition: GPUDialect.h:34
static Value getDim3Value(const gpu::KernelDim3 &dim3, unsigned pos)
Definition: SCFToGPU.cpp:58
Value constantOne(OpBuilder &builder, Location loc, Type tp)
Generates a 1-valued constant of the given type.
Definition: CodegenUtils.h:120
void addLegalDialect(StringRef name, Names... names)
Register the operations of the given dialects as legal.
void configureParallelLoopToGPULegality(ConversionTarget &target)
Configures the rewrite target such that only scf.parallel operations that are not rewritten by the pr...
Definition: SCFToGPU.cpp:674
This class represents an instance of an SSA value in the MLIR system, representing a computable value...
Definition: Value.h:85
static bool isConstantOne(Value value)
Definition: SCFToGPU.cpp:171
OpRewritePattern is a wrapper around RewritePattern that allows for matching and rewriting against an...
Definition: PatternMatch.h:355
void setInsertionPointToStart(Block *block)
Sets the insertion point to the start of the specified block.
Definition: Builders.h:377
U dyn_cast() const
Definition: Attributes.h:127
Specialization of arith.constant op that returns an integer of index type.
Definition: Arithmetic.h:80
Operation * getDefiningOp() const
If this value is the result of an operation, return the operation that defines it.
Definition: Value.cpp:20
Block * lookupOrDefault(Block *from) const
Lookup a mapped value within the map.
AffineExpr getAffineDimExpr(unsigned position)
Definition: Builders.cpp:309
This class implements the operand iterators for the Operation class.
Definition: ValueRange.h:40
void setInsertionPointToEnd(Block *block)
Sets the insertion point to the end of the specified block.
Definition: Builders.h:382
std::enable_if_t<!std::is_convertible< CallbackT, Twine >::value, LogicalResult > notifyMatchFailure(Location loc, CallbackT &&reasonCallback)
Used to notify the rewriter that the IR failed to be rewritten because of a match failure...
Definition: PatternMatch.h:512
static Value getOrEmitLowerBound(AffineForOp forOp, OpBuilder &builder)
Definition: SCFToGPU.cpp:91
This class describes a specific conversion target.
Block::iterator getInsertionPoint() const
Returns the current insertion point of the builder.
Definition: Builders.h:391
InFlightDiagnostic emitError(const Twine &message={})
Emit an error about fatal conditions with this operation, reporting up to any diagnostic handlers tha...
Definition: Operation.cpp:221
result_range getResults()
Definition: Operation.h:332
This class helps build Operations.
Definition: Builders.h:192
Value lowerAffineUpperBound(AffineForOp op, OpBuilder &builder)
Emit code that computes the upper bound of the given affine loop using standard arithmetic operations...
This class provides an abstraction over the different types of ranges over Values.
Definition: ValueRange.h:345
MLIRContext * getContext() const
static LogicalResult checkAffineLoopNestMappableImpl(AffineForOp forOp, unsigned numDims)
Definition: SCFToGPU.cpp:107