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Merged
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May 18, 2025
Merged

Restore #140171 with to_memref -> to_buffer #140355

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5e7fc62
Restore [mlir][bufferization] implement BufferizableOpInterface for c…
j2kun May 17, 2025
0832a86
to_memref -> to_buffer
j2kun May 17, 2025
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4 changes: 2 additions & 2 deletions mlir/lib/Dialect/Tensor/IR/TensorDialect.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -49,8 +49,8 @@ void TensorDialect::initialize() {
>();
addInterfaces<TensorInlinerInterface>();
declarePromisedInterfaces<
bufferization::BufferizableOpInterface, CastOp, CollapseShapeOp, DimOp,
EmptyOp, ExpandShapeOp, ExtractSliceOp, ExtractOp, FromElementsOp,
bufferization::BufferizableOpInterface, CastOp, CollapseShapeOp, ConcatOp,
DimOp, EmptyOp, ExpandShapeOp, ExtractSliceOp, ExtractOp, FromElementsOp,
GenerateOp, InsertOp, InsertSliceOp, PadOp, ParallelInsertSliceOp, RankOp,
ReshapeOp, SplatOp>();
declarePromisedInterfaces<transform::FindPayloadReplacementOpInterface,
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129 changes: 129 additions & 0 deletions mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1048,6 +1048,134 @@ struct SplatOpInterface
}
};

/// Bufferization of tensor.concat. Bufferizes to a new allocation that is
/// filled with copy ops. Similar to tensor.from_elements, but using memref.copy
/// on subviews instead of memref.store.
struct ConcatOpInterface
: public BufferizableOpInterface::ExternalModel<ConcatOpInterface,
tensor::ConcatOp> {

bool bufferizesToAllocation(Operation *op, Value value) const { return true; }

bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
return false;
}

bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
return true;
}

AliasingValueList getAliasingValues(Operation *op, OpOperand &opOperand,
const AnalysisState &state) const {
return {};
}

LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
const BufferizationOptions &options) const {
OpBuilder::InsertionGuard g(rewriter);
auto concatOp = cast<tensor::ConcatOp>(op);

// Allocate memory.
Location loc = op->getLoc();
FailureOr<Value> tensorAlloc = allocateTensorForShapedValue(
rewriter, loc, concatOp.getResult(), options,
/*copy=*/false);
if (failed(tensorAlloc))
return failure();
auto tensorType = cast<RankedTensorType>(tensorAlloc->getType());

// TODO: Implement memory space for this op.
if (options.defaultMemorySpaceFn(tensorType) != Attribute())
return op->emitError("memory space not implemented yet");

MemRefLayoutAttrInterface layout;
MemRefType memrefType =
MemRefType::get(concatOp.getResultType().getShape(),
concatOp.getResultType().getElementType(), layout);
Value dstBuffer = rewriter.create<bufferization::ToBufferOp>(
op->getLoc(), memrefType, *tensorAlloc);

// Extract the dimension for the concat op
uint64_t concatDim = concatOp.getDim();
bool dynamicConcatDim = false;

SmallVector<OpFoldResult> offsets(tensorType.getRank(),
rewriter.getIndexAttr(0));
SmallVector<OpFoldResult> strides(tensorType.getRank(),
rewriter.getIndexAttr(1));
SmallVector<OpFoldResult> sizes;

for (const auto &[dimIdx, dimSize] :
llvm::enumerate(tensorType.getShape())) {
if (dimSize == ShapedType::kDynamic) {
auto dimOp = rewriter.create<memref::DimOp>(loc, dstBuffer, dimIdx);
sizes.push_back(dimOp.getResult());
if (dimIdx == concatDim)
dynamicConcatDim = true;
} else {
sizes.push_back(rewriter.getIndexAttr(dimSize));
}
}

int64_t concatDimOffset = 0;
std::optional<Value> dynamicOffset;
std::optional<Value> dynamicSize;
if (dynamicConcatDim) {
// One or more operands have dynamic size, so we must accumulate the
// offset with arith ops.
dynamicOffset = rewriter.create<arith::ConstantIndexOp>(loc, 0);
}

for (auto operand : concatOp.getInputs()) {
// Get the buffer for the operand.
FailureOr<Value> srcBuffer = getBuffer(rewriter, operand, options);
if (failed(srcBuffer))
return failure();

// Each operand may have a different size along the concat dimension,
// so the offset on that axis must accumulate through the loop, and the
// size must change to the size of the current operand.
auto operandTensorType = cast<RankedTensorType>(operand.getType());
int64_t operandConcatDimSize = operandTensorType.getDimSize(concatDim);

if (dynamicConcatDim) {
offsets[concatDim] = dynamicOffset.value();
dynamicSize = rewriter.create<memref::DimOp>(loc, *srcBuffer, concatDim)
.getResult();
sizes[concatDim] = dynamicSize.value();
} else {
sizes[concatDim] = rewriter.getIndexAttr(operandConcatDimSize);
offsets[concatDim] = rewriter.getIndexAttr(concatDimOffset);
}

// Create a subview of the destination buffer.
auto dstMemrefType = cast<MemRefType>(memrefType);
MemRefType subviewMemRefType =
memref::SubViewOp::inferRankReducedResultType(
operandTensorType.getShape(), dstMemrefType, offsets, sizes,
strides);
Value subview = rewriter.create<memref::SubViewOp>(
loc, subviewMemRefType, dstBuffer, offsets, sizes, strides);

// Copy the source buffer into the destination subview.
if (failed(options.createMemCpy(rewriter, loc, *srcBuffer, subview)))
return failure();

if (dynamicConcatDim) {
dynamicOffset = rewriter.create<arith::AddIOp>(
loc, dynamicOffset.value(), dynamicSize.value());
} else {
concatDimOffset += operandConcatDimSize;
}
}

replaceOpWithBufferizedValues(rewriter, op, dstBuffer);
return success();
}
};

} // namespace
} // namespace tensor
} // namespace mlir
Expand All @@ -1057,6 +1185,7 @@ void mlir::tensor::registerBufferizableOpInterfaceExternalModels(
registry.addExtension(+[](MLIRContext *ctx, tensor::TensorDialect *dialect) {
CastOp::attachInterface<CastOpInterface>(*ctx);
CollapseShapeOp::attachInterface<CollapseShapeOpInterface>(*ctx);
ConcatOp::attachInterface<ConcatOpInterface>(*ctx);
DimOp::attachInterface<DimOpInterface>(*ctx);
EmptyOp::attachInterface<EmptyOpInterface>(*ctx);
ExpandShapeOp::attachInterface<ExpandShapeOpInterface>(*ctx);
Expand Down
91 changes: 91 additions & 0 deletions mlir/test/Dialect/Tensor/bufferize.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -615,6 +615,97 @@ func.func @tensor.splat(%f: f32) -> tensor<10x2x4xf32> {

// -----

// CHECK-LABEL: func @tensor.concat(
// CHECK-SAME: %[[F:.*]]: tensor<8xf32>)
// CHECK: %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
// CHECK: %[[ALLOC:.*]] = memref.alloc() {{.*}} : memref<16xf32>
// CHECK: %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0] [8] [1]
// CHECK: memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
// CHECK: %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][8] [8] [1]
// CHECK: memref.copy %[[F_MEMREF]], %[[SUBVIEW2]]
// CHECK: %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
// CHECK: return %[[RET]]
// CHECK: }
func.func @tensor.concat(%f: tensor<8xf32>) -> tensor<16xf32> {
%t = tensor.concat dim(0) %f, %f : (tensor<8xf32>, tensor<8xf32>) -> tensor<16xf32>
return %t : tensor<16xf32>
}

// -----

// CHECK-LABEL: func @tensor.concat_different_shapes(
// CHECK-SAME: %[[F:.*]]: tensor<8x4xf32>
// CHECK-SAME: %[[G:.*]]: tensor<8x5xf32>
// CHECK-DAG: %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
// CHECK-DAG: %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
// CHECK: %[[ALLOC:.*]] = memref.alloc() {{.*}} : memref<8x9xf32>
// CHECK: %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, 0] [8, 4] [1, 1]
// CHECK: memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
// CHECK: %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, 4] [8, 5] [1, 1]
// CHECK: memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
// CHECK: %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
// CHECK: return %[[RET]]
// CHECK: }
func.func @tensor.concat_different_shapes(%f: tensor<8x4xf32>, %g: tensor<8x5xf32>) -> tensor<8x9xf32> {
%t = tensor.concat dim(1) %f, %g : (tensor<8x4xf32>, tensor<8x5xf32>) -> tensor<8x9xf32>
return %t : tensor<8x9xf32>
}

// -----

// CHECK-LABEL: func @tensor.concat_dynamic(
// CHECK-SAME: %[[F:.*]]: tensor<8x?xf32>,
// CHECK-SAME: %[[G:.*]]: tensor<8x?xf32>
// CHECK-DAG: %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
// CHECK-DAG: %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[F_DIM:.*]] = memref.dim %[[F_MEMREF]], %[[c1]]
// CHECK-DAG: %[[G_DIM:.*]] = memref.dim %[[G_MEMREF]], %[[c1]]
// CHECK: %[[ALLOC:.*]] = memref.alloc
// CHECK-SAME: memref<8x?xf32>
// CHECK-DAG: %[[OFFSET:.*]] = arith.constant 0 : index
// CHECK: %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET]]] [8, %[[F_DIM]]] [1, 1]
// CHECK: memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
// CHECK: %[[OFFSET_2:.*]] = arith.addi %[[OFFSET]], %[[F_DIM]] : index
// CHECK: %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET_2]]] [8, %[[G_DIM]]] [1, 1]
// CHECK: memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
// CHECK: %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
// CHECK: return %[[RET]]
// CHECK: }
func.func @tensor.concat_dynamic(%f: tensor<8x?xf32>, %g: tensor<8x?xf32>) -> tensor<8x?xf32> {
%t = tensor.concat dim(1) %f, %g : (tensor<8x?xf32>, tensor<8x?xf32>) -> tensor<8x?xf32>
return %t : tensor<8x?xf32>
}

// -----

// CHECK-LABEL: func @tensor.concat_dynamic_nonconcat_dim(
// CHECK-SAME: %[[F:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[G:.*]]: tensor<?x?xf32>
// CHECK-DAG: %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
// CHECK-DAG: %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[F_DIM:.*]] = memref.dim %[[F_MEMREF]], %[[c1]]
// CHECK-DAG: %[[G_DIM:.*]] = memref.dim %[[G_MEMREF]], %[[c1]]
// CHECK: %[[ALLOC:.*]] = memref.alloc
// CHECK-SAME: memref<?x?xf32>
// CHECK-DAG: %[[NON_CONCAT_DIM:.*]] = memref.dim %[[ALLOC]], %[[c0]]
// CHECK: %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, %[[c0]]] [%[[NON_CONCAT_DIM]], %[[F_DIM]]] [1, 1]
// CHECK: memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
// CHECK: %[[OFFSET_2:.*]] = arith.addi %[[c0]], %[[F_DIM]] : index
// CHECK: %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET_2]]] [%[[NON_CONCAT_DIM]], %[[G_DIM]]] [1, 1]
// CHECK: memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
// CHECK: %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
// CHECK: return %[[RET]]
// CHECK: }
func.func @tensor.concat_dynamic_nonconcat_dim(%f: tensor<?x?xf32>, %g: tensor<?x?xf32>) -> tensor<?x?xf32> {
%t = tensor.concat dim(1) %f, %g : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
return %t : tensor<?x?xf32>
}

// -----

// CHECK-LABEL: func @tensor.splat_dynamic(
// CHECK-SAME: %[[F:[a-zA-Z0-9_]+]]: f32
// CHECK-SAME: %[[M:[a-zA-Z0-9_]+]]: index
Expand Down