[Mlir-commits] [mlir] 35c19fd - [mlir][vector] Support warp distribution of `transfer_read` with dependencies (#77779)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Fri Jan 12 02:55:41 PST 2024
Author: Matthias Springer
Date: 2024-01-12T11:55:37+01:00
New Revision: 35c19fdde2583e74d940f6cd47b97a5c28bfe368
URL: https://github.com/llvm/llvm-project/commit/35c19fdde2583e74d940f6cd47b97a5c28bfe368
DIFF: https://github.com/llvm/llvm-project/commit/35c19fdde2583e74d940f6cd47b97a5c28bfe368.diff
LOG: [mlir][vector] Support warp distribution of `transfer_read` with dependencies (#77779)
Support distribution of `vector.transfer_read` ops when operands are
defined inside of the region of `warp_execute_on_lane_0` (except for the
buffer from which the op is reading).
Such IR was previously not supported. This commit changes the
implementation such that indices and the padding value are also
distributed.
This commit simplifies the implementation considerably: the original
implementation created a new `transfer_read` op and then checked if this
new op is valid. If not, the rewrite pattern failed. This was a bit
hacky. It was also a violation of the rewrite pattern API (detected by
`MLIR_ENABLE_EXPENSIVE_PATTERN_API_CHECKS`) because the IR was modified,
but the pattern returned "failure".
Added:
Modified:
mlir/lib/Dialect/Vector/Transforms/VectorDistribute.cpp
mlir/test/Dialect/Vector/vector-warp-distribute.mlir
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/Vector/Transforms/VectorDistribute.cpp b/mlir/lib/Dialect/Vector/Transforms/VectorDistribute.cpp
index 074356ab425377..b369964bd01c51 100644
--- a/mlir/lib/Dialect/Vector/Transforms/VectorDistribute.cpp
+++ b/mlir/lib/Dialect/Vector/Transforms/VectorDistribute.cpp
@@ -819,9 +819,15 @@ struct WarpOpTransferRead : public OpRewritePattern<WarpExecuteOnLane0Op> {
return isa<vector::TransferReadOp>(op) && op->hasOneUse();
});
if (!operand)
- return failure();
+ return rewriter.notifyMatchFailure(
+ warpOp, "warp result is not a vector.transfer_read op");
auto read = operand->get().getDefiningOp<vector::TransferReadOp>();
+ // Source must be defined outside of the region.
+ if (!warpOp.isDefinedOutsideOfRegion(read.getSource()))
+ return rewriter.notifyMatchFailure(
+ read, "source must be defined outside of the region");
+
unsigned operandIndex = operand->getOperandNumber();
Value distributedVal = warpOp.getResult(operandIndex);
@@ -832,10 +838,25 @@ struct WarpOpTransferRead : public OpRewritePattern<WarpExecuteOnLane0Op> {
AffineMap map = calculateImplicitMap(sequentialType, distributedType);
AffineMap indexMap = map.compose(read.getPermutationMap());
- // Distribute the mask if present.
+ // Try to delinearize the lane ID to match the rank expected for
+ // distribution.
+ SmallVector<Value> delinearizedIds;
+ if (!delinearizeLaneId(rewriter, read.getLoc(), sequentialType.getShape(),
+ distributedType.getShape(), warpOp.getWarpSize(),
+ warpOp.getLaneid(), delinearizedIds)) {
+ return rewriter.notifyMatchFailure(
+ read, "cannot delinearize lane ID for distribution");
+ }
+ assert(!delinearizedIds.empty() || map.getNumResults() == 0);
+
+ // Distribute indices and the mask (if present).
OpBuilder::InsertionGuard g(rewriter);
- WarpExecuteOnLane0Op newWarpOp = warpOp;
- Value newMask = read.getMask();
+ SmallVector<Value> additionalResults(indices.begin(), indices.end());
+ SmallVector<Type> additionalResultTypes(indices.size(),
+ rewriter.getIndexType());
+ additionalResults.push_back(read.getPadding());
+ additionalResultTypes.push_back(read.getPadding().getType());
+
bool hasMask = false;
if (read.getMask()) {
hasMask = true;
@@ -846,42 +867,26 @@ struct WarpOpTransferRead : public OpRewritePattern<WarpExecuteOnLane0Op> {
// by shape information on the warp op, and thus requires materializing
// the permutation in IR.
if (!mlir::compressUnusedDims(read.getPermutationMap()).isIdentity())
- return failure();
+ return rewriter.notifyMatchFailure(
+ read, "non-trivial permutation maps not supported");
VectorType maskType =
getDistributedType(read.getMaskType(), map, warpOp.getWarpSize());
- SmallVector<size_t> newRetIndices;
- newWarpOp = moveRegionToNewWarpOpAndAppendReturns(
- rewriter, warpOp, ValueRange{read.getMask()}, TypeRange{maskType},
- newRetIndices);
- newMask = newWarpOp.getResult(newRetIndices[0]);
- distributedVal = newWarpOp.getResult(operandIndex);
- } else {
- // This pattern does not actually change the warp op directly. Instead it
- // just rewrites a new transfer read (when not masked) outside of the warp
- // op and replaces the correponding result. There are then follow up
- // patterns to erase now dead results of the warp op. This erasure allows
- // propagation to continue, but this pattern on its own never actually
- // tells the pattern rewriter that the warp op "changed." Notify the
- // rewriter here that the warp op is changing. Similar situations are
- // noted in following patterns.
- rewriter.startRootUpdate(warpOp);
+ additionalResults.push_back(read.getMask());
+ additionalResultTypes.push_back(maskType);
}
- rewriter.setInsertionPointAfter(newWarpOp);
+ SmallVector<size_t> newRetIndices;
+ WarpExecuteOnLane0Op newWarpOp = moveRegionToNewWarpOpAndAppendReturns(
+ rewriter, warpOp, additionalResults, additionalResultTypes,
+ newRetIndices);
+ distributedVal = newWarpOp.getResult(operandIndex);
- // Try to delinearize the lane ID to match the rank expected for
- // distribution.
- SmallVector<Value> delinearizedIds;
- if (!delinearizeLaneId(rewriter, read.getLoc(), sequentialType.getShape(),
- distributedType.getShape(), newWarpOp.getWarpSize(),
- newWarpOp.getLaneid(), delinearizedIds)) {
- if (!hasMask)
- rewriter.cancelRootUpdate(warpOp);
- return rewriter.notifyMatchFailure(
- read, "cannot delinearize lane ID for distribution");
- }
- assert(!delinearizedIds.empty() || map.getNumResults() == 0);
+ // Distributed indices were appended first.
+ SmallVector<Value> newIndices;
+ for (int64_t i = 0, e = indices.size(); i < e; ++i)
+ newIndices.push_back(newWarpOp.getResult(newRetIndices[i]));
+ rewriter.setInsertionPointAfter(newWarpOp);
for (auto it : llvm::zip_equal(indexMap.getResults(), map.getResults())) {
AffineExpr d0, d1;
bindDims(read.getContext(), d0, d1);
@@ -891,42 +896,23 @@ struct WarpOpTransferRead : public OpRewritePattern<WarpExecuteOnLane0Op> {
unsigned indexPos = indexExpr.getPosition();
unsigned vectorPos = cast<AffineDimExpr>(std::get<1>(it)).getPosition();
int64_t scale = distributedType.getDimSize(vectorPos);
- indices[indexPos] = affine::makeComposedAffineApply(
+ newIndices[indexPos] = affine::makeComposedAffineApply(
rewriter, read.getLoc(), d0 + scale * d1,
- {indices[indexPos], delinearizedIds[vectorPos]});
+ {newIndices[indexPos], delinearizedIds[vectorPos]});
}
+
+ // Distributed padding value was appended right after the indices.
+ Value newPadding = newWarpOp.getResult(newRetIndices[indices.size()]);
+ // Distributed mask value was added at the end (if the op has a mask).
+ Value newMask =
+ hasMask ? newWarpOp.getResult(newRetIndices[newRetIndices.size() - 1])
+ : Value();
auto newRead = rewriter.create<vector::TransferReadOp>(
- read.getLoc(), distributedVal.getType(), read.getSource(), indices,
- read.getPermutationMapAttr(), read.getPadding(), newMask,
+ read.getLoc(), distributedVal.getType(), read.getSource(), newIndices,
+ read.getPermutationMapAttr(), newPadding, newMask,
read.getInBoundsAttr());
- // Check that the produced operation is legal.
- // The transfer op may be reading from values that are defined within
- // warpOp's body, which is illegal.
- // We do the check late because incdices may be changed by
- // makeComposeAffineApply. This rewrite may remove dependencies from
- // warpOp's body.
- // E.g., warpop {
- // %idx = affine.apply...[%outsideDef]
- // ... = transfer_read ...[%idx]
- // }
- // will be rewritten in:
- // warpop {
- // }
- // %new_idx = affine.apply...[%outsideDef]
- // ... = transfer_read ...[%new_idx]
- if (!llvm::all_of(newRead->getOperands(), [&](Value value) {
- return (newRead.getMask() && value == newRead.getMask()) ||
- newWarpOp.isDefinedOutsideOfRegion(value);
- })) {
- if (!hasMask)
- rewriter.cancelRootUpdate(warpOp);
- return failure();
- }
-
rewriter.replaceAllUsesWith(distributedVal, newRead);
- if (!hasMask)
- rewriter.finalizeRootUpdate(warpOp);
return success();
}
};
@@ -1315,6 +1301,12 @@ struct WarpOpExtractElement : public OpRewritePattern<WarpExecuteOnLane0Op> {
unsigned int operandNumber = operand->getOperandNumber();
auto extractOp = operand->get().getDefiningOp<vector::ExtractElementOp>();
VectorType extractSrcType = extractOp.getSourceVectorType();
+ // TODO: Supported shuffle types should be parameterizable, similar to
+ // `WarpShuffleFromIdxFn`.
+ if (!extractSrcType.getElementType().isF32() &&
+ !extractSrcType.getElementType().isInteger(32))
+ return rewriter.notifyMatchFailure(
+ extractOp, "only f32/i32 element types are supported");
bool is0dOrVec1Extract = extractSrcType.getNumElements() == 1;
Type elType = extractSrcType.getElementType();
VectorType distributedVecType;
diff --git a/mlir/test/Dialect/Vector/vector-warp-distribute.mlir b/mlir/test/Dialect/Vector/vector-warp-distribute.mlir
index e04fa64f0f8a70..9072603734879e 100644
--- a/mlir/test/Dialect/Vector/vector-warp-distribute.mlir
+++ b/mlir/test/Dialect/Vector/vector-warp-distribute.mlir
@@ -899,6 +899,25 @@ func.func @vector_extractelement_1d(%laneid: index, %pos: index) -> (f32) {
// -----
+// Index-typed values cannot be shuffled at the moment.
+
+// CHECK-PROP-LABEL: func.func @vector_extractelement_1d_index(
+// CHECK-PROP: vector.warp_execute_on_lane_0(%{{.*}})[32] -> (index) {
+// CHECK-PROP: "some_def"
+// CHECK-PROP: vector.extractelement
+// CHECK-PROP: vector.yield {{.*}} : index
+// CHECK-PROP: }
+func.func @vector_extractelement_1d_index(%laneid: index, %pos: index) -> (index) {
+ %r = vector.warp_execute_on_lane_0(%laneid)[32] -> (index) {
+ %0 = "some_def"() : () -> (vector<96xindex>)
+ %1 = vector.extractelement %0[%pos : index] : vector<96xindex>
+ vector.yield %1 : index
+ }
+ return %r : index
+}
+
+// -----
+
// CHECK-PROP: func @lane_dependent_warp_propagate_read
// CHECK-PROP-SAME: %[[ID:.*]]: index
func.func @lane_dependent_warp_propagate_read(
@@ -1248,12 +1267,12 @@ func.func @vector_insert_2d_broadcast(%laneid: index) -> (vector<4x96xf32>) {
// -----
-// Check that we don't propagate transfer_reads that have dependencies on
-// values inside the warp_execute_on_lane_0.
-// In this case, propagating would create transfer_read that depends on the
-// extractelment defined in the body.
+// Make sure that all operands of the transfer_read op are properly propagated.
+// The vector.extractelement op cannot be propagated because index-typed
+// shuffles are not supported at the moment.
-// CHECK-PROP-LABEL: func @transfer_read_no_prop(
+// CHECK-PROP: #[[$MAP:.*]] = affine_map<()[s0] -> (s0 * 2)>
+// CHECK-PROP-LABEL: func @transfer_read_prop_operands(
// CHECK-PROP-SAME: %[[IN2:[^ :]*]]: vector<1x2xindex>,
// CHECK-PROP-SAME: %[[AR1:[^ :]*]]: memref<1x4x2xi32>,
// CHECK-PROP-SAME: %[[AR2:[^ :]*]]: memref<1x4x1024xf32>)
@@ -1264,10 +1283,11 @@ func.func @vector_insert_2d_broadcast(%laneid: index) -> (vector<4x96xf32>) {
// CHECK-PROP: %[[EXTRACT:.*]] = vector.extract %[[GATHER]][0] : vector<64xi32> from vector<1x64xi32>
// CHECK-PROP: %[[CAST:.*]] = arith.index_cast %[[EXTRACT]] : vector<64xi32> to vector<64xindex>
// CHECK-PROP: %[[EXTRACTELT:.*]] = vector.extractelement %[[CAST]][{{.*}}: i32] : vector<64xindex>
-// CHECK-PROP: %[[TRANSFERREAD:.*]] = vector.transfer_read %[[AR2]][%[[C0]], %[[EXTRACTELT]], %[[C0]]],
-// CHECK-PROP: vector.yield %[[TRANSFERREAD]] : vector<64xf32>
-// CHECK-PROP: return %[[W]]
-func.func @transfer_read_no_prop(%in2: vector<1x2xindex>, %ar1 : memref<1x4x2xi32>, %ar2 : memref<1x4x1024xf32>)-> vector<2xf32> {
+// CHECK-PROP: vector.yield %[[EXTRACTELT]] : index
+// CHECK-PROP: %[[APPLY:.*]] = affine.apply #[[$MAP]]()[%[[THREADID]]]
+// CHECK-PROP: %[[TRANSFERREAD:.*]] = vector.transfer_read %[[AR2]][%[[C0]], %[[W]], %[[APPLY]]],
+// CHECK-PROP: return %[[TRANSFERREAD]]
+func.func @transfer_read_prop_operands(%in2: vector<1x2xindex>, %ar1 : memref<1x4x2xi32>, %ar2 : memref<1x4x1024xf32>)-> vector<2xf32> {
%0 = gpu.thread_id x
%c0_i32 = arith.constant 0 : i32
%c0 = arith.constant 0 : index
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