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[mlir][SCF] Retire SCF-specific to_memref/to_tensor canonicalization patterns (#74551)

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The partial bufferization framework has been replaced with One-Shot
Bufferize. SCF-specific canonicalization patterns for
`to_memref`/`to_tensor` are no longer needed.
This commit is contained in:
Matthias Springer
2023-12-07 08:24:17 +09:00
committed by GitHub
parent 23d402e5b7
commit 77f5b33c46
4 changed files with 4 additions and 182 deletions
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3
mlir/lib/Dialect/SCF/IR/CMakeLists.txt
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@@ -11,12 +11,13 @@ add_mlir_dialect_library(MLIRSCFDialect
LINK_LIBS PUBLIC
MLIRArithDialect
MLIRBufferizationDialect
MLIRControlFlowDialect
MLIRDialectUtils
MLIRFunctionInterfaces
MLIRIR
MLIRLoopLikeInterface
MLIRSideEffectInterfaces
MLIRTensorDialect
MLIRValueBoundsOpInterface
)

132
mlir/lib/Dialect/SCF/IR/SCF.cpp
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@@ -9,7 +9,6 @@
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/DeviceMappingInterface.h"
@@ -1082,139 +1081,12 @@ struct ForOpTensorCastFolder : public OpRewritePattern<ForOp> {
}
};
/// Canonicalize the iter_args of an scf::ForOp that involve a
/// `bufferization.to_tensor` and for which only the last loop iteration is
/// actually visible outside of the loop. The canonicalization looks for a
/// pattern such as:
/// ```
/// %t0 = ... : tensor_type
/// %0 = scf.for ... iter_args(%bb0 : %t0) -> (tensor_type) {
/// ...
/// // %m is either buffer_cast(%bb00) or defined above the loop
/// %m... : memref_type
/// ... // uses of %m with potential inplace updates
/// %new_tensor = bufferization.to_tensor %m : memref_type
/// ...
/// scf.yield %new_tensor : tensor_type
/// }
/// ```
///
/// `%bb0` may have either 0 or 1 use. If it has 1 use it must be exactly a
/// `%m = buffer_cast %bb0` op that feeds into the yielded
/// `bufferization.to_tensor` op.
///
/// If no aliasing write to the memref `%m`, from which `%new_tensor`is loaded,
/// occurs between `bufferization.to_tensor and yield then the value %0
/// visible outside of the loop is the last `bufferization.to_tensor`
/// produced in the loop.
///
/// For now, we approximate the absence of aliasing by only supporting the case
/// when the bufferization.to_tensor is the operation immediately preceding
/// the yield.
//
/// The canonicalization rewrites the pattern as:
/// ```
/// // %m is either a buffer_cast or defined above
/// %m... : memref_type
/// scf.for ... iter_args(%bb0 : %t0) -> (tensor_type) {
/// ... // uses of %m with potential inplace updates
/// scf.yield %bb0: tensor_type
/// }
/// %0 = bufferization.to_tensor %m : memref_type
/// ```
///
/// A later bbArg canonicalization will further rewrite as:
/// ```
/// // %m is either a buffer_cast or defined above
/// %m... : memref_type
/// scf.for ... { // no iter_args
/// ... // uses of %m with potential inplace updates
/// }
/// %0 = bufferization.to_tensor %m : memref_type
/// ```
struct LastTensorLoadCanonicalization : public OpRewritePattern<ForOp> {
using OpRewritePattern<ForOp>::OpRewritePattern;
LogicalResult matchAndRewrite(ForOp forOp,
PatternRewriter &rewriter) const override {
assert(std::next(forOp.getRegion().begin()) == forOp.getRegion().end() &&
"unexpected multiple blocks");
Location loc = forOp.getLoc();
DenseMap<Value, Value> replacements;
for (BlockArgument bbArg : forOp.getRegionIterArgs()) {
unsigned idx = bbArg.getArgNumber() - /*numIv=*/1;
auto yieldOp =
cast<scf::YieldOp>(forOp.getRegion().front().getTerminator());
Value yieldVal = yieldOp->getOperand(idx);
auto tensorLoadOp = yieldVal.getDefiningOp<bufferization::ToTensorOp>();
bool isTensor = llvm::isa<TensorType>(bbArg.getType());
bufferization::ToMemrefOp tensorToMemref;
// Either bbArg has no use or it has a single buffer_cast use.
if (bbArg.hasOneUse())
tensorToMemref =
dyn_cast<bufferization::ToMemrefOp>(*bbArg.getUsers().begin());
if (!isTensor || !tensorLoadOp || (!bbArg.use_empty() && !tensorToMemref))
continue;
// If tensorToMemref is present, it must feed into the `ToTensorOp`.
if (tensorToMemref && tensorLoadOp.getMemref() != tensorToMemref)
continue;
// TODO: Any aliasing write of tensorLoadOp.memref() nested under `forOp`
// must be before `ToTensorOp` in the block so that the lastWrite
// property is not subject to additional side-effects.
// For now, we only support the case when ToTensorOp appears
// immediately before the terminator.
if (tensorLoadOp->getNextNode() != yieldOp)
continue;
// Clone the optional tensorToMemref before forOp.
if (tensorToMemref) {
rewriter.setInsertionPoint(forOp);
rewriter.replaceOpWithNewOp<bufferization::ToMemrefOp>(
tensorToMemref, tensorToMemref.getMemref().getType(),
tensorToMemref.getTensor());
}
// Clone the tensorLoad after forOp.
rewriter.setInsertionPointAfter(forOp);
Value newTensorLoad = rewriter.create<bufferization::ToTensorOp>(
loc, tensorLoadOp.getMemref());
Value forOpResult = forOp.getResult(bbArg.getArgNumber() - /*iv=*/1);
replacements.insert(std::make_pair(forOpResult, newTensorLoad));
// Make the terminator just yield the bbArg, the old tensorLoadOp + the
// old bbArg (that is now directly yielded) will canonicalize away.
rewriter.startRootUpdate(yieldOp);
yieldOp.setOperand(idx, bbArg);
rewriter.finalizeRootUpdate(yieldOp);
}
if (replacements.empty())
return failure();
// We want to replace a subset of the results of `forOp`. rewriter.replaceOp
// replaces the whole op and erase it unconditionally. This is wrong for
// `forOp` as it generally contains ops with side effects.
// Instead, use `rewriter.replaceOpWithIf`.
SmallVector<Value> newResults;
newResults.reserve(forOp.getNumResults());
for (Value v : forOp.getResults()) {
auto it = replacements.find(v);
newResults.push_back((it != replacements.end()) ? it->second : v);
}
unsigned idx = 0;
rewriter.replaceOpWithIf(forOp, newResults, [&](OpOperand &op) {
return op.get() != newResults[idx++];
});
return success();
}
};
} // namespace
void ForOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<ForOpIterArgsFolder, SimplifyTrivialLoops,
LastTensorLoadCanonicalization, ForOpTensorCastFolder>(context);
results.add<ForOpIterArgsFolder, SimplifyTrivialLoops, ForOpTensorCastFolder>(
context);
}
std::optional<APInt> ForOp::getConstantStep() {

50
mlir/test/Dialect/SCF/canonicalize.mlir
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@@ -773,56 +773,6 @@ func.func @remove_empty_parallel_loop(%lb: index, %ub: index, %s: index) {
// -----
func.func private @process(%0 : memref<128x128xf32>)
func.func private @process_tensor(%0 : tensor<128x128xf32>) -> memref<128x128xf32>
// CHECK-LABEL: last_value
// CHECK-SAME: %[[T0:[0-9a-z]*]]: tensor<128x128xf32>
// CHECK-SAME: %[[T1:[0-9a-z]*]]: tensor<128x128xf32>
// CHECK-SAME: %[[T2:[0-9a-z]*]]: tensor<128x128xf32>
// CHECK-SAME: %[[M0:[0-9a-z]*]]: memref<128x128xf32>
func.func @last_value(%t0: tensor<128x128xf32>, %t1: tensor<128x128xf32>,
%t2: tensor<128x128xf32>, %m0: memref<128x128xf32>,
%lb : index, %ub : index, %step : index)
-> (tensor<128x128xf32>, tensor<128x128xf32>, tensor<128x128xf32>)
{
// CHECK-NEXT: %[[M1:.*]] = bufferization.to_memref %[[T1]] : memref<128x128xf32>
// CHECK-NEXT: %[[FOR_RES:.*]] = scf.for {{.*}} iter_args(%[[BBARG_T2:.*]] = %[[T2]]) -> (tensor<128x128xf32>) {
%0:3 = scf.for %arg0 = %lb to %ub step %step iter_args(%arg1 = %t0, %arg2 = %t1, %arg3 = %t2)
-> (tensor<128x128xf32>, tensor<128x128xf32>, tensor<128x128xf32>)
{
%m1 = bufferization.to_memref %arg2 : memref<128x128xf32>
// CHECK-NEXT: call @process(%[[M0]]) : (memref<128x128xf32>) -> ()
func.call @process(%m0) : (memref<128x128xf32>) -> ()
// CHECK-NEXT: call @process(%[[M1]]) : (memref<128x128xf32>) -> ()
func.call @process(%m1) : (memref<128x128xf32>) -> ()
// This does not hoist (fails the bbArg has at most a single check).
// CHECK-NEXT: %[[T:.*]] = func.call @process_tensor(%[[BBARG_T2]]) : (tensor<128x128xf32>) -> memref<128x128xf32>
// CHECK-NEXT: %[[YIELD_T:.*]] = bufferization.to_tensor %[[T:.*]]
%m2 = func.call @process_tensor(%arg3): (tensor<128x128xf32>) -> memref<128x128xf32>
%3 = bufferization.to_tensor %m2 : memref<128x128xf32>
// All this stuff goes away, incrementally
%1 = bufferization.to_tensor %m0 : memref<128x128xf32>
%2 = bufferization.to_tensor %m1 : memref<128x128xf32>
// CHECK-NEXT: scf.yield %[[YIELD_T]] : tensor<128x128xf32>
scf.yield %1, %2, %3 : tensor<128x128xf32>, tensor<128x128xf32>, tensor<128x128xf32>
// CHECK-NEXT: }
}
// CHECK-NEXT: %[[R0:.*]] = bufferization.to_tensor %[[M0]] : memref<128x128xf32>
// CHECK-NEXT: %[[R1:.*]] = bufferization.to_tensor %[[M1]] : memref<128x128xf32>
// CHECK-NEXT: return %[[R0]], %[[R1]], %[[FOR_RES]] : tensor<128x128xf32>, tensor<128x128xf32>, tensor<128x128xf32>
return %0#0, %0#1, %0#2 : tensor<128x128xf32>, tensor<128x128xf32>, tensor<128x128xf32>
}
// -----
// CHECK-LABEL: fold_away_iter_with_no_use_and_yielded_input
// CHECK-SAME: %[[A0:[0-9a-z]*]]: i32
func.func @fold_away_iter_with_no_use_and_yielded_input(%arg0 : i32,

1
utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
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@@ -3994,7 +3994,6 @@ cc_library(
deps = [
":ArithDialect",
":ArithUtils",
":BufferizationDialect",
":ControlFlowDialect",
":ControlFlowInterfaces",
":DestinationStyleOpInterface",