[mlir][AMDGPU] Add canonicalization pattern to pack scales for ScaledMFMAOp #155951
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@llvm/pr-subscribers-mlir-gpu @llvm/pr-subscribers-backend-amdgpu Author: Muzammil (Muzammiluddin-Syed-ECE) ChangesThe ScaledMFMAOp accepts scales as a vector of 4 bytes ( This is fixed by identifying when single byte extractions are performed and rewriting them into extractions of 4-byte vectors. Example: to Full diff: https://github.com/llvm/llvm-project/pull/155951.diff 4 Files Affected:
diff --git a/mlir/include/mlir/Dialect/AMDGPU/IR/AMDGPU.td b/mlir/include/mlir/Dialect/AMDGPU/IR/AMDGPU.td
index 2ccf350a359a8..a24a918357f2d 100644
--- a/mlir/include/mlir/Dialect/AMDGPU/IR/AMDGPU.td
+++ b/mlir/include/mlir/Dialect/AMDGPU/IR/AMDGPU.td
@@ -1048,5 +1048,6 @@ def AMDGPU_ScaledMFMAOp :
attr-dict
`:` type($scalesA) `,` type($sourceA) `,` type($scalesB) `,` type($sourceB) `,` type($destC)
}];
+ let hasCanonicalizer = 1;
}
#endif // AMDGPU
diff --git a/mlir/lib/Dialect/AMDGPU/IR/AMDGPUDialect.cpp b/mlir/lib/Dialect/AMDGPU/IR/AMDGPUDialect.cpp
index 11a40d663a201..4107ec53a0988 100644
--- a/mlir/lib/Dialect/AMDGPU/IR/AMDGPUDialect.cpp
+++ b/mlir/lib/Dialect/AMDGPU/IR/AMDGPUDialect.cpp
@@ -17,6 +17,7 @@
#include "mlir/Dialect/LLVMIR/ROCDLDialect.h"
#include "mlir/Dialect/MemRef/Utils/MemRefUtils.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
+#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/Diagnostics.h"
@@ -28,6 +29,7 @@
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/TypeSwitch.h"
+#include <cstdint>
#include <limits>
#include <optional>
@@ -631,6 +633,146 @@ LogicalResult TransposeLoadOp::verify() {
return success();
}
+//===----------------------------------------------------------------------===//
+// ScaledMFMAOp
+//===----------------------------------------------------------------------===//
+
+namespace {
+/// Check if the scales input is used in other scaled mfma's while they exist.
+/// If theyre unused then pack the scales.
+struct PackScales final : OpRewritePattern<ScaledMFMAOp> {
+ using OpRewritePattern::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(ScaledMFMAOp op,
+ PatternRewriter &rewriter) const override {
+ Location loc = op.getLoc();
+ // If this use of a scale has a non zero opsel, packing has already been
+ // done.
+ auto checkIfUnpackable = [&](OpOperand &op) {
+ if (auto smfma = dyn_cast<ScaledMFMAOp>(op.getOwner())) {
+ switch (op.getOperandNumber()) {
+ case 3:
+ return smfma.getScalesIdxA() != 0;
+ break;
+ case 4:
+ return smfma.getScalesIdxB() != 0;
+ break;
+ default:
+ return true;
+ break;
+ }
+ }
+ };
+
+ auto setOpsel = [&](unsigned idx, int64_t val) {
+ switch (idx) {
+ case 3:
+ return op.setScalesIdxA(val);
+ break;
+ case 4:
+ return op.setScalesIdxB(val);
+ break;
+ default:
+ break;
+ }
+ };
+
+ // Obtain flat index from offsets and shape.
+ auto getIdxFromExtract = [](vector::ExtractOp op) {
+ ShapedType ty = dyn_cast<ShapedType>(op.getOperand(0).getType());
+ int cumul = 1;
+ int idx = 0;
+ for (auto [offset, size] :
+ reverse(llvm::zip_equal(op.getStaticPosition(), ty.getShape()))) {
+ idx += offset * cumul;
+ cumul *= size;
+ }
+ return idx;
+ };
+
+ // Obtain offsets for new shape from flat index.
+ auto getOffsetsFromIdx = [](int64_t idx, Type ty) {
+ SmallVector<int64_t> res;
+ ShapedType shapedty = static_cast<ShapedType>(ty);
+ int64_t numElements = shapedty.getNumElements();
+ for (auto size : shapedty.getShape()) {
+ numElements /= size;
+ res.push_back(idx / numElements);
+ idx -= (idx / numElements) * size;
+ }
+ return res;
+ };
+
+ // For every scale operand of this ScaledMFMAOp, if the scale follows the
+ // following pattern:
+ //
+ // %unit = vector.extract %ScaleSrc[offsets] : f8E8M0FNU from vector<?x?x?xf8E8M0FNU>
+ // %scale = vector.insert %unit, ... : f8E8M0FNU into vector<4xf8E8M0FNU>
+ // amdgpu.scaled_mfma(%scale[0] * ...
+ //
+ // rewrite to:
+ //
+ // %reshaped = vector.shape_cast %ScaleSrc : vector<?x?x?xf8E8M0FNU> to vector<?x4xf8E8M0FNU>
+ // %scale = vector.extract %reshaped[?] : vector<4xf8E8M0FNU> from vector<?x4xf8E8M0FNU>
+ // amdgpu.scaled_mfma(%scale[0-3] * ...
+ //
+ // This creates duplicate shape_casts for every use but these will be removed in CSE.
+ for (auto opIdx : SmallVector<int64_t>({3, 4})) {
+ auto insertOp = op.getOperand(opIdx).getDefiningOp<vector::InsertOp>();
+ if (!insertOp) {
+ return failure();
+ }
+ if (llvm::any_of(insertOp.getResult().getUses(), checkIfUnpackable)) {
+ return failure();
+ }
+
+ auto extractOp =
+ insertOp.getOperand(0).getDefiningOp<vector::ExtractOp>();
+ if (!extractOp) {
+ return failure();
+ }
+
+ Value scaleSrc = extractOp.getOperand(0);
+ auto stype = dyn_cast<ShapedType>(scaleSrc.getType());
+ if (!stype) {
+ return failure();
+ }
+ // We do not handle dynamic dims yet, assume that the input is padded to
+ // a static shape now.
+ if (llvm::any_of(llvm::seq<int64_t>(0, stype.getRank()),
+ [&](int64_t i) { return stype.isDynamicDim(i); })) {
+ return failure();
+ }
+
+ int64_t numElements = stype.getNumElements();
+ if (numElements <= 4) {
+ return failure();
+ }
+
+ Type newSrcType = VectorType::get(
+ SmallVector<int64_t>({numElements / 4, 4}), stype.getElementType());
+ Value newScaleSrc =
+ rewriter.create<vector::ShapeCastOp>(loc, newSrcType, scaleSrc);
+ int64_t idx = getIdxFromExtract(extractOp);
+ SmallVector<int64_t> offsets(getOffsetsFromIdx(idx, newSrcType));
+ auto scaleTy = VectorType::get({4}, stype.getElementType());
+ Value extract = rewriter.create<vector::ExtractStridedSliceOp>(
+ loc, newScaleSrc, SmallVector<int64_t>{offsets[0], 0},
+ SmallVector<int64_t>{1, 4}, SmallVector<int64_t>{1, 1});
+ Value scale = rewriter.create<vector::ShapeCastOp>(loc, scaleTy, extract);
+ op.setOperand(opIdx, scale);
+ setOpsel(opIdx, offsets[1]);
+ }
+ return success();
+ }
+};
+} // namespace
+
+void ScaledMFMAOp::getCanonicalizationPatterns(RewritePatternSet &results,
+ MLIRContext *context) {
+ results.add<PackScales>(context);
+}
+
#include "mlir/Dialect/AMDGPU/IR/AMDGPUEnums.cpp.inc"
#define GET_ATTRDEF_CLASSES
diff --git a/mlir/lib/Dialect/AMDGPU/IR/CMakeLists.txt b/mlir/lib/Dialect/AMDGPU/IR/CMakeLists.txt
index 2a019954c8356..5d14a05945e95 100644
--- a/mlir/lib/Dialect/AMDGPU/IR/CMakeLists.txt
+++ b/mlir/lib/Dialect/AMDGPU/IR/CMakeLists.txt
@@ -14,6 +14,7 @@ add_mlir_dialect_library(MLIRAMDGPUDialect
MLIRROCDLDialect
# Needed for GPU address space enum definition
MLIRGPUDialect
+ MLIRVectorDialect
MLIRIR
MLIRSideEffectInterfaces
MLIRMemRefUtils
diff --git a/mlir/test/Dialect/AMDGPU/canonicalize.mlir b/mlir/test/Dialect/AMDGPU/canonicalize.mlir
index 5501ad42dbd90..75cbf29c95f29 100644
--- a/mlir/test/Dialect/AMDGPU/canonicalize.mlir
+++ b/mlir/test/Dialect/AMDGPU/canonicalize.mlir
@@ -159,3 +159,28 @@ func.func @fold_gather_to_lds_of_cast_dest(%global: memref<128x72xf32, 1>, %lds:
: f32, memref<128x72xf32, 1>, memref<?x?xf32, 3>
func.return
}
+
+// -----
+
+// CHECK-LABEL: func @scaled_mfma
+// CHECK: %[[SCALE_1:.*]] = vector.extract %{{.*}}[0] : vector<4xf8E8M0FNU> from vector<4x4xf8E8M0FNU>
+// CHECK: %[[SCALE_2:.*]] = vector.extract %{{.*}}[1] : vector<4xf8E8M0FNU> from vector<4x4xf8E8M0FNU>
+// CHECK: amdgpu.scaled_mfma(%[[SCALE_1]][3] * %{{.*}}) * (%[[SCALE_2]][2] * %{{.*}}) {{.*}}
+// CHECK: %[[SCALE_3:.*]] = vector.extract %{{.*}}[2] : vector<4xf8E8M0FNU> from vector<4x4xf8E8M0FNU>
+// CHECK: %[[SCALE_4:.*]] = vector.extract %{{.*}}[3] : vector<4xf8E8M0FNU> from vector<4x4xf8E8M0FNU>
+// CHECK: amdgpu.scaled_mfma(%[[SCALE_3]][1] * %{{.*}}) * (%[[SCALE_4]][0] * %{{.*}}) {{.*}}
+func.func @scaled_mfma(%opA: vector<32xf4E2M1FN>, %opB: vector<32xf4E2M1FN>, %scalesA: vector<2x1x8x1xf8E8M0FNU>, %scalesB: vector<2x1x8x1xf8E8M0FNU>) -> (vector<4xf32>, vector<4xf32>) {
+ %cst_0 = arith.constant dense<0.000000e+00> : vector<4xf32>
+ %cst_1 = arith.constant dense<5.877470e-39> : vector<4xf8E8M0FNU>
+ %scaleA = vector.extract %scalesA[0, 0, 3, 0] : f8E8M0FNU from vector<2x1x8x1xf8E8M0FNU>
+ %sA = vector.insert %scaleA, %cst_1 [0] : f8E8M0FNU into vector<4xf8E8M0FNU>
+ %scaleB = vector.extract %scalesB[0, 0, 6, 0] : f8E8M0FNU from vector<2x1x8x1xf8E8M0FNU>
+ %sB = vector.insert %scaleB, %cst_1 [0] : f8E8M0FNU into vector<4xf8E8M0FNU>
+ %res_0 = amdgpu.scaled_mfma(%sA[0] * %opA) * (%sB[0] * %opB) + %cst_0 {k = 128 : i32, m = 16 : i32, n = 16 : i32} : vector<4xf8E8M0FNU>, vector<32xf4E2M1FN>, vector<4xf8E8M0FNU>, vector<32xf4E2M1FN>, vector<4xf32>
+ %scaleC = vector.extract %scalesA[1, 0, 1, 0] : f8E8M0FNU from vector<2x1x8x1xf8E8M0FNU>
+ %sC = vector.insert %scaleC, %cst_1 [0] : f8E8M0FNU into vector<4xf8E8M0FNU>
+ %scaleD = vector.extract %scalesB[1, 0, 4, 0] : f8E8M0FNU from vector<2x1x8x1xf8E8M0FNU>
+ %sD = vector.insert %scaleD, %cst_1 [0] : f8E8M0FNU into vector<4xf8E8M0FNU>
+ %res_1 = amdgpu.scaled_mfma(%sC[0] * %opA) * (%sD[0] * %opB) + %cst_0 {k = 128 : i32, m = 16 : i32, n = 16 : i32} : vector<4xf8E8M0FNU>, vector<32xf4E2M1FN>, vector<4xf8E8M0FNU>, vector<32xf4E2M1FN>, vector<4xf32>
+ return %res_0, %res_1 : vector<4xf32>, vector<4xf32>
+}
|
|
✅ With the latest revision this PR passed the C/C++ code formatter. |
Signed-off-by: Muzammiluddin Syed <[email protected]>
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Improving choice of class used, from SmallVector to ArrayRef (https://llvm.org/docs/ProgrammersManual.html#llvm-adt-arrayref-h). Also infer template types when possible. Leftover from #155951. --------- Signed-off-by: Muzammiluddin Syed <[email protected]>
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…C (#163770) Improving choice of class used, from SmallVector to ArrayRef (https://llvm.org/docs/ProgrammersManual.html#llvm-adt-arrayref-h). Also infer template types when possible. Leftover from llvm/llvm-project#155951. --------- Signed-off-by: Muzammiluddin Syed <[email protected]>
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The ScaledMFMAOp accepts scales as a vector of 4 bytes (
vector<4xf8E8M0FNU>) that can be stored in a single register with a particular scale accessed using theOpSelattribute. Currently, we only use one byte in this 4-byte vector, resulting in 3 wasted registers.This is fixed by identifying when single byte extractions are performed and rewriting them into extractions of 4-byte vectors.
Example:
to