[Mlir-commits] [mlir] [MLIR][Linalg] Add specialization for linalg.broadcast (PR #104684)
Javed Absar
llvmlistbot at llvm.org
Sat Aug 17 13:42:54 PDT 2024
https://github.com/javedabsar1 created https://github.com/llvm/llvm-project/pull/104684
Specialize `linalg.genereic` that are are `linalg.broadcast`
>From 9a1f106e7b477b102e979d9f572e18c8693fe8ba Mon Sep 17 00:00:00 2001
From: Javed Absar <javed.absar at gmail.com>
Date: Sat, 17 Aug 2024 16:13:25 -0400
Subject: [PATCH] [MLIR][Linalg] Add specialization for linalg.broadcast
Specialize `linalg.genereic` that are are `linalg.broadcast`
---
.../mlir/Dialect/Linalg/IR/LinalgInterfaces.h | 5 ++
.../Dialect/Linalg/IR/LinalgInterfaces.cpp | 73 +++++++++++++++++--
.../Dialect/Linalg/Transforms/Specialize.cpp | 15 ++++
.../Dialect/Linalg/roundtrip-broadcast.mlir | 36 +++++++++
.../Linalg/transform-op-specialize.mlir | 12 ---
5 files changed, 121 insertions(+), 20 deletions(-)
create mode 100644 mlir/test/Dialect/Linalg/roundtrip-broadcast.mlir
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgInterfaces.h b/mlir/include/mlir/Dialect/Linalg/IR/LinalgInterfaces.h
index 08afdf373f014a..221155a31c34da 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgInterfaces.h
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgInterfaces.h
@@ -131,6 +131,11 @@ bool isaElemwiseSingleBinaryOpInterface(GenericOp genericOp);
/// Returns the scalar fill value if true.
std::optional<Value> isaFillOpInterface(GenericOp genericOp);
+/// Checks whether `genericOp` is semantically equivalent to a
+/// `linalg.broadcast`. Returns broadcast dimension if true.
+std::optional<SmallVector<int64_t>>
+isaBroadcastOpInterface(GenericOp genericOp);
+
namespace detail {
/// Returns true if the block contains a contraction of the following form:
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
index 6ee1810c2ff2b9..95c2fbbb00bc2c 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
@@ -22,6 +22,7 @@
#include "llvm/ADT/SmallBitVector.h"
#include "llvm/ADT/SmallVector.h"
#include <algorithm>
+#include <numeric>
using namespace mlir;
using namespace mlir::linalg;
@@ -29,6 +30,24 @@ using namespace mlir::linalg;
/// Include the definitions of the copy operation interface.
#include "mlir/Dialect/Linalg/IR/LinalgInterfaces.cpp.inc"
+namespace {
+/// Check linalg generic with single input output has
+/// body that is just a yield op yielding input value.
+static bool bodyIsJustYieldOp(GenericOp genericOp) {
+ assert(genericOp.getNumDpsInputs() == 1 && genericOp.getNumDpsInits() == 1 &&
+ "expected single input output to linalg.generic");
+ Block *body = genericOp.getBody();
+ if (body->getOperations().size() != 1)
+ return false;
+
+ auto yieldOp = dyn_cast<linalg::YieldOp>(body->back());
+ if (!yieldOp || yieldOp.getNumOperands() != 1 ||
+ yieldOp->getOperand(0) != body->getArgument(0))
+ return false;
+ return true;
+}
+} // namespace
+
//===----------------------------------------------------------------------===//
// Interface utility functions
//===----------------------------------------------------------------------===//
@@ -52,7 +71,6 @@ bool linalg::detail::canOpOperandsBeDroppedImpl(
//===----------------------------------------------------------------------===//
// CopyOpInterface implementation
//===----------------------------------------------------------------------===//
-
bool linalg::isaCopyOpInterface(LinalgOp linalgOp) {
// Structural.
if (linalgOp.getNumParallelLoops() != linalgOp.getNumLoops())
@@ -85,18 +103,57 @@ std::optional<Value> linalg::isaFillOpInterface(GenericOp genericOp) {
return std::nullopt;
OpOperand *value = genericOp.getDpsInputOperand(0);
- if (!genericOp.isScalar(value))
+ if (!genericOp.isScalar(value) || !bodyIsJustYieldOp(genericOp))
+ return std::nullopt;
+ return value->get();
+}
+
+//===----------------------------------------------------------------------===//
+// BroadcastOpInterface implementation
+//===----------------------------------------------------------------------===//
+std::optional<SmallVector<int64_t>>
+linalg::isaBroadcastOpInterface(GenericOp genericOp) {
+
+ // Structural.
+ if ((genericOp.getNumParallelLoops() != genericOp.getNumLoops()) ||
+ genericOp.getNumDpsInputs() != 1 || genericOp.getNumDpsInits() != 1 ||
+ !bodyIsJustYieldOp(genericOp))
return std::nullopt;
- Block *body = genericOp.getBody();
- if (body->getOperations().size() != 1)
+ auto t0 = genericOp.getDpsInputOperand(0)->get().getType();
+ auto t1 = genericOp.getDpsInitOperand(0)->get().getType();
+ if (!isa<MemRefType, RankedTensorType>(t0) ||
+ !isa<MemRefType, RankedTensorType>(t1))
return std::nullopt;
- auto yieldOp = dyn_cast<linalg::YieldOp>(body->back());
- if (!yieldOp || yieldOp.getNumOperands() != 1 ||
- yieldOp->getOperand(0) != body->getArgument(0))
+ // Check output is identity map. Injective function could also be
+ // a permutation of indices and expressible in linalg.generic but
+ // is not expressible for named broadcast op.
+ auto dstMap = genericOp.getIndexingMapsArray()[1];
+ if (!dstMap.isIdentity())
return std::nullopt;
- return value->get();
+
+ SmallVector<int64_t> position;
+ auto srcMap = genericOp.getIndexingMapsArray()[0];
+
+ // Check input map is monotonically increasing DimIds.
+ for (unsigned i = 0; i < srcMap.getNumResults(); ++i) {
+ auto expr = llvm::dyn_cast<AffineDimExpr>(srcMap.getResults()[i]);
+ if (!expr)
+ return std::nullopt;
+ int64_t pos = expr.getPosition();
+ if (i > 0 && pos <= position[i - 1])
+ return std::nullopt;
+ position.push_back(expr.getPosition());
+ }
+
+ SmallVector<int64_t> broadcastedDims;
+ auto numDims = srcMap.getNumDims();
+ for (auto dim : llvm::seq<int64_t>(0, numDims)) {
+ if (!llvm::is_contained(position, dim))
+ broadcastedDims.push_back(dim);
+ }
+ return broadcastedDims;
}
//===----------------------------------------------------------------------===//
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
index 4d7b748d7200e2..801622752c8404 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
@@ -259,18 +259,31 @@ static FailureOr<LinalgOp> specializeLinalgContractions(RewriterBase &rewriter,
//===----------------------------------------------------------------------===//
FailureOr<LinalgOp> mlir::linalg::specializeGenericOp(RewriterBase &rewriter,
GenericOp genericOp) {
+ // Copy
if (isaCopyOpInterface(genericOp)) {
LinalgOp namedOp = rewriter.replaceOpWithNewOp<CopyOp>(
genericOp, genericOp.getDpsInputs()[0], genericOp.getDpsInits()[0]);
return namedOp;
}
+ // Fill
if (isaFillOpInterface(genericOp)) {
LinalgOp namedOp = rewriter.replaceOpWithNewOp<FillOp>(
genericOp, genericOp.getDpsInputs()[0], genericOp.getDpsInits()[0]);
return namedOp;
}
+ // Broadcast
+ std::optional<SmallVector<int64_t>> equivalentToBroadcast
+ = isaBroadcastOpInterface(genericOp);
+ if (equivalentToBroadcast) {
+ auto dims = *equivalentToBroadcast;
+ LinalgOp namedOp = rewriter.replaceOpWithNewOp<BroadcastOp>(
+ genericOp, genericOp.getDpsInputs()[0], genericOp.getDpsInits()[0], dims);
+ return namedOp;
+ }
+
+ // Elementwise Unary
if (isaElemwiseSingleUnaryOpInterface(genericOp)) {
Operation *op = &genericOp.getBody()->front();
if (isa<math::ExpOp>(op)) {
@@ -279,6 +292,7 @@ FailureOr<LinalgOp> mlir::linalg::specializeGenericOp(RewriterBase &rewriter,
}
}
+ // Elementwise Binary
if (isaElemwiseSingleBinaryOpInterface(genericOp)) {
bool swap = areBinOpsSwapped(genericOp);
Operation *op = &genericOp.getBody()->front();
@@ -300,6 +314,7 @@ FailureOr<LinalgOp> mlir::linalg::specializeGenericOp(RewriterBase &rewriter,
}
}
+ // Contraction
if (isaContractionOpInterface(genericOp)) {
return specializeLinalgContractions(rewriter, genericOp);
}
diff --git a/mlir/test/Dialect/Linalg/roundtrip-broadcast.mlir b/mlir/test/Dialect/Linalg/roundtrip-broadcast.mlir
new file mode 100644
index 00000000000000..10d7ba826f79f9
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/roundtrip-broadcast.mlir
@@ -0,0 +1,36 @@
+// RUN: mlir-opt %s -linalg-generalize-named-ops | mlir-opt --linalg-specialize-generic-ops | FileCheck %s
+
+// CHECK-LABEL: broadcast_first_dimension
+// CHECK-SAME: %[[A:.+]]: tensor<?x?xf32>, %[[Out:.+]]: tensor<?x?x?xf32>)
+// CHECK-NOT: linalg.generic
+// CHECK: %broadcasted = linalg.broadcast ins(%[[A]] : tensor<?x?xf32>) outs(%[[Out]] : tensor<?x?x?xf32>) dimensions = [0]
+//
+func.func @broadcast_first_dimension(%A: tensor<?x?xf32>, %Out: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
+ %res = linalg.broadcast ins(%A: tensor<?x?xf32>) outs(%Out: tensor<?x?x?xf32>) dimensions = [0]
+ return %res : tensor<?x?x?xf32>
+}
+
+// CHECK-LABEL: broadcast_mid_dimension
+// CHECK-SAME: %[[A:.+]]: tensor<3x5xf32>, %[[Out:.+]]: tensor<3x4x5xf32>)
+// CHECK-NOT: linalg.generic
+// CHECK: %broadcasted = linalg.broadcast ins(%[[A]] : tensor<3x5xf32>) outs(%[[Out]] : tensor<3x4x5xf32>) dimensions = [1]
+//
+func.func @broadcast_mid_dimension(%A: tensor<3x5xf32>, %Out: tensor<3x4x5xf32>) -> tensor<3x4x5xf32> {
+ %res = linalg.broadcast ins(%A: tensor<3x5xf32>) outs(%Out: tensor<3x4x5xf32>) dimensions = [1]
+ return %res : tensor<3x4x5xf32>
+}
+
+
+// CHECK-LABEL: broadcast_multiple_dimensions
+// CHECK-SAME: %[[A:.+]]: tensor<4x5x7xf32>, %[[Out:.+]]: tensor<3x4x5x6x7x8x9xf32>)
+// CHECK-NOT: linalg.generic
+// CHECK: %broadcasted = linalg.broadcast ins(%[[A]] : tensor<4x5x7xf32>) outs(%[[Out]] : tensor<3x4x5x6x7x8x9xf32>) dimensions = [0, 3, 5, 6]
+//
+func.func @broadcast_multiple_dimensions(%A: tensor<4x5x7xf32>, %Out: tensor<3x4x5x6x7x8x9xf32>) -> tensor<3x4x5x6x7x8x9xf32> {
+ %res = linalg.broadcast ins(%A: tensor<4x5x7xf32>) outs(%Out: tensor<3x4x5x6x7x8x9xf32>) dimensions = [0,3,5,6]
+ return %res : tensor<3x4x5x6x7x8x9xf32>
+}
+
+
+
+
diff --git a/mlir/test/Dialect/Linalg/transform-op-specialize.mlir b/mlir/test/Dialect/Linalg/transform-op-specialize.mlir
index 35679db7412f30..31f2f6b1ab513f 100644
--- a/mlir/test/Dialect/Linalg/transform-op-specialize.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-specialize.mlir
@@ -4,18 +4,6 @@
#map1 = affine_map<(d0, d1) -> (d0)>
#map2 = affine_map<(d0, d1) -> (d1, d0)>
-func.func @broadcast_copy_expect_no_match(%arg0: memref<?xf32>, %arg1: memref<?x?xf32>) {
- // expected-note @below {{when applied to this op}}
- linalg.generic {
- indexing_maps = [#map1, #map],
- iterator_types = ["parallel", "parallel"]}
- ins(%arg0 : memref<?xf32>) outs(%arg1 : memref<?x?xf32>) {
- ^bb0(%in: f32, %out: f32):
- linalg.yield %in : f32
- }
- return
-}
-
func.func @not_a_copy_expect_no_match(%arg0: memref<?x?xf32>, %arg1: memref<?x?xf32>) {
// expected-note @below {{when applied to this op}}
linalg.generic {
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