| //===- ShardOps.cpp - Shard Dialect Operations ----------------------------===// |
| // |
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| // See https://llvm.org/LICENSE.txt for license information. |
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| // |
| //===----------------------------------------------------------------------===// |
| |
| #include "mlir/Dialect/Shard/IR/ShardOps.h" |
| |
| #include "mlir/Dialect/Arith/IR/Arith.h" |
| #include "mlir/Dialect/Shard/IR/ShardDialect.h" |
| #include "mlir/Dialect/Utils/StaticValueUtils.h" |
| #include "mlir/IR/Attributes.h" |
| #include "mlir/IR/BuiltinAttributes.h" |
| #include "mlir/IR/BuiltinTypeInterfaces.h" |
| #include "mlir/IR/BuiltinTypes.h" |
| #include "mlir/IR/Diagnostics.h" |
| #include "mlir/IR/DialectImplementation.h" |
| #include "mlir/IR/IRMapping.h" |
| #include "mlir/IR/Location.h" |
| #include "mlir/IR/PatternMatch.h" |
| #include "mlir/IR/TypeUtilities.h" |
| #include "mlir/IR/Value.h" |
| #include "mlir/Interfaces/ViewLikeInterface.h" |
| #include "mlir/Support/LLVM.h" |
| #include "mlir/Transforms/InliningUtils.h" |
| #include "llvm/ADT/ArrayRef.h" |
| #include "llvm/ADT/STLExtras.h" |
| #include "llvm/ADT/SmallSet.h" |
| #include "llvm/ADT/SmallVector.h" |
| #include "llvm/ADT/TypeSwitch.h" |
| #include <algorithm> |
| #include <functional> |
| #include <iterator> |
| #include <numeric> |
| #include <optional> |
| #include <utility> |
| |
| #define DEBUG_TYPE "shard-ops" |
| |
| using namespace mlir; |
| using namespace mlir::shard; |
| |
| #include "mlir/Dialect/Shard/IR/ShardDialect.cpp.inc" |
| |
| namespace { |
| |
| struct DimensionSize { |
| static DimensionSize dynamic() { return DimensionSize(ShapedType::kDynamic); } |
| DimensionSize(int64_t val) : val(val) {} |
| int64_t value() const { return val; } |
| operator int64_t() const { return val; } |
| bool isDynamic() const { return ShapedType::isDynamic(val); } |
| |
| private: |
| int64_t val; |
| }; |
| |
| } // namespace |
| |
| static DimensionSize operator/(DimensionSize lhs, DimensionSize rhs) { |
| if (lhs.isDynamic() || rhs.isDynamic()) { |
| return DimensionSize::dynamic(); |
| } |
| return lhs.value() / rhs.value(); |
| } |
| |
| static DimensionSize operator*(DimensionSize lhs, DimensionSize rhs) { |
| if (lhs.isDynamic() || rhs.isDynamic()) { |
| return DimensionSize::dynamic(); |
| } |
| return lhs.value() * rhs.value(); |
| } |
| |
| SmallVector<Value> |
| mlir::shard::getMixedAsValues(OpBuilder b, const Location &loc, |
| llvm::ArrayRef<int64_t> statics, |
| ValueRange dynamics, Type type) { |
| SmallVector<Value> values; |
| auto dyn = dynamics.begin(); |
| Type i64 = b.getI64Type(); |
| if (!type) |
| type = i64; |
| assert((i64 == type || b.getIndexType() == type) && |
| "expected an i64 or an intex type"); |
| for (auto s : statics) { |
| if (s == ShapedType::kDynamic) { |
| values.emplace_back(*(dyn++)); |
| } else { |
| TypedAttr val = type == i64 ? b.getI64IntegerAttr(s) : b.getIndexAttr(s); |
| values.emplace_back(arith::ConstantOp::create(b, loc, type, val)); |
| } |
| } |
| return values; |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // Inliner |
| //===----------------------------------------------------------------------===// |
| |
| namespace { |
| struct ShardInlinerinterface : public DialectInlinerInterface { |
| using DialectInlinerInterface::DialectInlinerInterface; |
| // Currently no restrictions are encoded for inlining. |
| bool isLegalToInline(Operation *, Operation *, bool) const final { |
| return true; |
| } |
| bool isLegalToInline(Region *, Region *, bool, IRMapping &) const final { |
| return true; |
| } |
| bool isLegalToInline(Operation *, Region *, bool, IRMapping &) const final { |
| return true; |
| } |
| }; |
| } // namespace |
| |
| //===----------------------------------------------------------------------===// |
| // Shard dialect |
| //===----------------------------------------------------------------------===// |
| |
| void ShardDialect::initialize() { |
| addOperations< |
| #define GET_OP_LIST |
| #include "mlir/Dialect/Shard/IR/ShardOps.cpp.inc" |
| >(); |
| addAttributes< |
| #define GET_ATTRDEF_LIST |
| #include "mlir/Dialect/Shard/IR/ShardAttributes.cpp.inc" |
| >(); |
| addTypes< |
| #define GET_TYPEDEF_LIST |
| #include "mlir/Dialect/Shard/IR/ShardTypes.cpp.inc" |
| >(); |
| addInterface<ShardInlinerinterface>(); |
| } |
| |
| Operation *ShardDialect::materializeConstant(OpBuilder &builder, |
| Attribute value, Type type, |
| Location loc) { |
| return arith::ConstantOp::materialize(builder, value, type, loc); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // Shard utilities |
| //===----------------------------------------------------------------------===// |
| |
| static FailureOr<GridOp> getGridAndVerify(Operation *op, |
| FlatSymbolRefAttr gridSymbol, |
| SymbolTableCollection &symbolTable) { |
| shard::GridOp grid = getGridOrNull(op, gridSymbol, symbolTable); |
| if (!grid) { |
| return op->emitError() << "Undefined required grid symbol \"" |
| << gridSymbol.getValue() << "\"."; |
| } |
| |
| return grid; |
| } |
| |
| template <typename It> |
| static bool isUnique(It begin, It end) { |
| if (begin == end) { |
| return true; |
| } |
| It next = std::next(begin); |
| if (next == end) { |
| return true; |
| } |
| for (; next != end; ++next, ++begin) { |
| if (*begin == *next) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| static LogicalResult verifyGridAxes(Location loc, ArrayRef<GridAxis> axes, |
| GridOp grid) { |
| SmallVector<GridAxis> sorted = llvm::to_vector(axes); |
| llvm::sort(sorted); |
| if (!isUnique(sorted.begin(), sorted.end())) { |
| return emitError(loc) << "Grid axes contains duplicate elements."; |
| } |
| |
| GridAxis rank = grid.getRank(); |
| for (auto axis : axes) { |
| if (axis >= rank || axis < 0) { |
| return emitError(loc) |
| << "0-based grid axis index " << axis |
| << " is out of bounds. The referenced grid \"" << grid.getSymName() |
| << "\" is of rank " << rank << "."; |
| } |
| } |
| |
| return success(); |
| } |
| |
| template <typename Op> |
| static FailureOr<GridOp> |
| getGridAndVerifyAxes(Op op, SymbolTableCollection &symbolTable) { |
| auto grid = |
| ::getGridAndVerify(op.getOperation(), op.getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyGridAxes(op.getLoc(), op.getGridAxes(), grid.value()))) { |
| return failure(); |
| } |
| return grid; |
| } |
| |
| template <typename InShape, typename GridShape, typename SplitAxes, |
| typename OutShape> |
| static void shardShape(const InShape &inShape, const GridShape &gridShape, |
| const SplitAxes &splitAxes, OutShape &outShape, |
| ArrayRef<int64_t> shardedDimsOffsets = {}, |
| ArrayRef<int64_t> haloSizes = {}) { |
| // 0d tensors cannot be sharded and must get replicated |
| if (inShape.empty()) { |
| assert(outShape.empty()); |
| return; |
| } |
| |
| std::copy(llvm::adl_begin(inShape), llvm::adl_end(inShape), |
| llvm::adl_begin(outShape)); |
| |
| if (!shardedDimsOffsets.empty()) { |
| auto isDynShape = ShapedType::isDynamicShape(gridShape); |
| uint64_t pos = 1; |
| for (auto [tensorAxis, innerSplitAxes] : llvm::enumerate(splitAxes)) { |
| if (!innerSplitAxes.empty()) { |
| auto sz = shardedDimsOffsets[pos]; |
| bool same = !isDynShape; |
| if (same) { |
| // Find sharded dims in shardedDimsOffsets with same static size on |
| // all devices. Use kDynamic for dimensions with dynamic or |
| // non-uniform offs in shardedDimsOffsets. |
| uint64_t numShards = 0; |
| for (auto i : innerSplitAxes.asArrayRef()) { |
| numShards += gridShape[i]; |
| } |
| for (size_t i = 1; i < numShards; ++i) { |
| if (shardedDimsOffsets[pos + i] - shardedDimsOffsets[pos + i - 1] != |
| sz) { |
| same = false; |
| break; |
| } |
| } |
| pos += numShards + 1; |
| } |
| outShape[tensorAxis] = same ? sz : ShapedType::kDynamic; |
| } |
| } |
| } else { |
| for (auto [tensorAxis, innerSplitAxes] : llvm::enumerate(splitAxes)) { |
| outShape[tensorAxis] = shardDimension( |
| inShape[tensorAxis], |
| collectiveProcessGroupSize(innerSplitAxes.asArrayRef(), gridShape)); |
| } |
| |
| if (!haloSizes.empty()) { |
| // add halo sizes if requested |
| int haloAxis = 0; |
| for (auto [tensorAxis, innerSplitAxes] : llvm::enumerate(splitAxes)) { |
| if (ShapedType::isStatic(outShape[tensorAxis]) && |
| !innerSplitAxes.empty()) { |
| if (haloSizes[haloAxis * 2] >= 0 && |
| haloSizes[haloAxis * 2 + 1] >= 0) { |
| outShape[tensorAxis] += |
| haloSizes[haloAxis * 2] + haloSizes[haloAxis * 2 + 1]; |
| ++haloAxis; |
| } else { |
| outShape[tensorAxis] = ShapedType::kDynamic; |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| ShapedType shard::shardShapedType(ShapedType shape, GridOp grid, |
| Sharding sharding) { |
| using Dim = std::decay_t<decltype(shape.getDimSize(0))>; |
| SmallVector<Dim> resShapeArr(shape.getShape().size()); |
| shardShape(shape.getShape(), grid.getShape(), sharding.getSplitAxes(), |
| resShapeArr, sharding.getStaticShardedDimsOffsets(), |
| sharding.getStaticHaloSizes()); |
| return shape.clone(resShapeArr); |
| } |
| |
| Type shard::shardType(Type type, GridOp grid, Sharding sharding) { |
| RankedTensorType rankedTensorType = dyn_cast<RankedTensorType>(type); |
| if (rankedTensorType && !rankedTensorType.getShape().empty()) { |
| return shardShapedType(rankedTensorType, grid, sharding); |
| } |
| return type; |
| } |
| |
| static void maybeInsertTargetShardingAnnotationImpl(Sharding sharding, |
| Value &operandValue, |
| Operation *operandOp, |
| OpBuilder &builder, |
| ShardOp &newShardOp) { |
| OpBuilder::InsertionGuard insertionGuard(builder); |
| builder.setInsertionPointAfterValue(operandValue); |
| ShardOp shardOp = dyn_cast<ShardOp>(operandOp); |
| if (shardOp && sharding == shardOp.getSharding() && |
| !shardOp.getAnnotateForUsers()) { |
| // No need for anything if the correct sharding is already set. |
| if (!newShardOp) { |
| newShardOp = shardOp; |
| } |
| return; |
| } |
| |
| if (!newShardOp) { |
| auto shardingOp = |
| ShardingOp::create(builder, operandValue.getLoc(), sharding); |
| newShardOp = ShardOp::create(builder, operandValue.getLoc(), operandValue, |
| shardingOp, |
| /*annotate_for_users*/ false); |
| } |
| operandValue.replaceUsesWithIf( |
| newShardOp, [operandOp, operandValue](OpOperand &use) { |
| return use.getOwner() == operandOp && use.get() == operandValue; |
| }); |
| |
| if (!shardOp || shardOp.getAnnotateForUsers()) { |
| return; |
| } |
| |
| auto newShardOp2 = ShardOp::create(builder, operandValue.getLoc(), newShardOp, |
| newShardOp.getSharding(), |
| /*annotate_for_users*/ true); |
| newShardOp.getResult().replaceAllUsesExcept(newShardOp2, newShardOp2); |
| } |
| |
| void mlir::shard::maybeInsertTargetShardingAnnotation(Sharding sharding, |
| OpResult result, |
| OpBuilder &builder) { |
| ShardOp newShardOp; |
| SmallVector<std::pair<Value, Operation *>> uses; |
| for (auto &use : result.getUses()) { |
| uses.emplace_back(use.get(), use.getOwner()); |
| } |
| for (auto &[operandValue, operandOp] : uses) { |
| maybeInsertTargetShardingAnnotationImpl(sharding, operandValue, operandOp, |
| builder, newShardOp); |
| } |
| } |
| |
| void mlir::shard::maybeInsertSourceShardingAnnotation(Sharding sharding, |
| OpOperand &operand, |
| OpBuilder &builder) { |
| OpBuilder::InsertionGuard insertionGuard(builder); |
| Value operandValue = operand.get(); |
| Operation *operandSrcOp = operandValue.getDefiningOp(); |
| bool isBlockArg = !operandSrcOp; |
| { |
| [[maybe_unused]] auto opType = |
| dyn_cast<mlir::RankedTensorType>(operandValue.getType()); |
| assert(!opType || opType.getRank() > 0 || isFullReplication(sharding)); |
| } |
| if (!isa<RankedTensorType>(operandValue.getType()) && operandSrcOp && |
| operandSrcOp->hasTrait<OpTrait::ConstantLike>()) { |
| return; |
| } |
| |
| Operation *operandOp = operand.getOwner(); |
| ShardOp shardOp = dyn_cast_or_null<ShardOp>(operandSrcOp); |
| |
| if (shardOp && sharding == shardOp.getSharding() && |
| shardOp.getAnnotateForUsers()) { |
| // No need for anything the correct sharding is already set. |
| return; |
| } |
| |
| builder.setInsertionPoint(operandOp); |
| auto shardingOp = |
| ShardingOp::create(builder, operand.get().getLoc(), sharding); |
| auto newShardOp = |
| ShardOp::create(builder, operandValue.getLoc(), operandValue, shardingOp, |
| /*annotate_for_users*/ true); |
| IRRewriter rewriter(builder); |
| rewriter.replaceUsesWithIf( |
| operandValue, newShardOp, [operandOp, operandValue](OpOperand &use) { |
| return use.getOwner() == operandOp && use.get() == operandValue; |
| }); |
| |
| if (isBlockArg || !shardOp || !shardOp.getAnnotateForUsers()) { |
| // No need for resharding. |
| return; |
| } |
| |
| builder.setInsertionPoint(newShardOp); |
| auto newPreceedingShardOp = |
| ShardOp::create(builder, operandValue.getLoc(), operandValue, shardingOp, |
| /*annotate_for_users*/ false); |
| rewriter.replaceUsesWithIf( |
| newShardOp.getSrc(), newPreceedingShardOp, [&newShardOp](OpOperand &use) { |
| return use.getOwner() == newShardOp.getOperation(); |
| }); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.grid op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult GridOp::verify() { |
| int64_t rank = getRank(); |
| |
| if (rank <= 0) |
| return emitOpError("rank of grid is expected to be a positive integer"); |
| |
| for (int64_t dimSize : getShape()) { |
| if (dimSize < 0 && ShapedType::isStatic(dimSize)) |
| return emitOpError("dimension size of a grid is expected to be " |
| "non-negative or dynamic"); |
| } |
| |
| return success(); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.grid_shape op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| GridShapeOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = ::getGridAndVerify(getOperation(), getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyGridAxes(getLoc(), getAxes(), grid.value()))) { |
| return failure(); |
| } |
| |
| size_t expectedResultsCount = |
| getAxes().empty() ? grid->getRank() : getAxes().size(); |
| if (getResult().size() != expectedResultsCount) { |
| return emitError() << "Unexpected number of results " << getResult().size() |
| << ". Expected " << expectedResultsCount << "."; |
| } |
| |
| return success(); |
| } |
| |
| void GridShapeOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| GridOp grid) { |
| build(odsBuilder, odsState, grid, SmallVector<GridAxis>()); |
| } |
| |
| void GridShapeOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| GridOp grid, ArrayRef<GridAxis> axes) { |
| build(odsBuilder, odsState, |
| SmallVector<Type>(axes.empty() ? grid.getRank() : axes.size(), |
| odsBuilder.getIndexType()), |
| grid.getSymName(), GridAxesAttr::get(odsBuilder.getContext(), axes)); |
| } |
| |
| void GridShapeOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| StringRef grid, ArrayRef<GridAxis> axes) { |
| assert(!axes.empty()); |
| build(odsBuilder, odsState, |
| SmallVector<Type>(axes.size(), odsBuilder.getIndexType()), grid, |
| GridAxesAttr::get(odsBuilder.getContext(), axes)); |
| } |
| |
| void GridShapeOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResults()[0], "grid_shape"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.sharding |
| //===----------------------------------------------------------------------===// |
| |
| void ShardingOp::build(::mlir::OpBuilder &b, ::mlir::OperationState &odsState, |
| FlatSymbolRefAttr grid, |
| ArrayRef<GridAxesAttr> split_axes, |
| ArrayRef<int64_t> static_halos, |
| ArrayRef<int64_t> static_offsets) { |
| return build( |
| b, odsState, grid, GridAxesArrayAttr::get(b.getContext(), split_axes), |
| ::mlir::DenseI64ArrayAttr::get(b.getContext(), static_halos), {}, |
| ::mlir::DenseI64ArrayAttr::get(b.getContext(), static_offsets), {}); |
| } |
| |
| void ShardingOp::build(::mlir::OpBuilder &b, ::mlir::OperationState &odsState, |
| llvm::StringRef grid, ArrayRef<GridAxesAttr> split_axes, |
| ArrayRef<int64_t> static_halos, |
| ArrayRef<int64_t> static_offsets) { |
| return build(b, odsState, FlatSymbolRefAttr::get(b.getContext(), grid), |
| GridAxesArrayAttr::get(b.getContext(), split_axes), |
| ::mlir::DenseI64ArrayAttr::get(b.getContext(), static_halos), {}, |
| ::mlir::DenseI64ArrayAttr::get(b.getContext(), static_offsets), |
| {}); |
| } |
| |
| void ShardingOp::build( |
| ::mlir::OpBuilder &b, ::mlir::OperationState &odsState, |
| FlatSymbolRefAttr grid, ArrayRef<GridAxesAttr> split_axes, |
| ::mlir::ArrayRef<::mlir::OpFoldResult> halo_sizes, |
| ::mlir::ArrayRef<::mlir::OpFoldResult> sharded_dims_offsets) { |
| mlir::SmallVector<int64_t> staticHalos, staticDims; |
| mlir::SmallVector<mlir::Value> dynamicHalos, dynamicDims; |
| dispatchIndexOpFoldResults(halo_sizes, dynamicHalos, staticHalos); |
| dispatchIndexOpFoldResults(sharded_dims_offsets, dynamicDims, staticDims); |
| return build( |
| b, odsState, grid, GridAxesArrayAttr::get(b.getContext(), split_axes), |
| ::mlir::DenseI64ArrayAttr::get(b.getContext(), staticHalos), dynamicHalos, |
| ::mlir::DenseI64ArrayAttr::get(b.getContext(), staticDims), dynamicDims); |
| } |
| |
| void ShardingOp::build(::mlir::OpBuilder &b, ::mlir::OperationState &odsState, |
| mlir::shard::Sharding from) { |
| |
| build(b, odsState, ShardingType::get(b.getContext()), from.getGridAttr(), |
| GridAxesArrayAttr::get(b.getContext(), from.getSplitAxes()), |
| from.getStaticShardedDimsOffsets().empty() |
| ? DenseI64ArrayAttr() |
| : b.getDenseI64ArrayAttr(from.getStaticShardedDimsOffsets()), |
| from.getDynamicShardedDimsOffsets(), |
| from.getStaticHaloSizes().empty() |
| ? DenseI64ArrayAttr() |
| : b.getDenseI64ArrayAttr(from.getStaticHaloSizes()), |
| from.getDynamicHaloSizes()); |
| } |
| |
| LogicalResult ShardingOp::verify() { |
| llvm::SmallSet<GridAxis, 4> visitedAxes; |
| |
| auto checkGridAxis = [&](ArrayRef<GridAxis> axesArray) -> LogicalResult { |
| for (GridAxis axis : axesArray) { |
| if (axis < 0) |
| return emitError() << "grid axis is expected to be non-negative"; |
| if (!visitedAxes.insert(axis).second) |
| return emitError() << "grid axis duplicated"; |
| } |
| return success(); |
| }; |
| |
| for (auto subAxes : getSplitAxes().getAxes()) { |
| ArrayRef<GridAxis> subAxesArray = subAxes.asArrayRef(); |
| if (failed(checkGridAxis(subAxesArray))) |
| return failure(); |
| } |
| |
| if (!getStaticHaloSizes().empty() && !getStaticShardedDimsOffsets().empty()) { |
| return emitOpError("halo sizes and shard offsets are mutually exclusive"); |
| } |
| |
| if (!getStaticHaloSizes().empty()) { |
| auto numSplitAxes = getSplitAxes().getAxes().size(); |
| for (auto splitAxis : getSplitAxes().getAxes()) { |
| if (splitAxis.empty()) { |
| --numSplitAxes; |
| } |
| } |
| if (getStaticHaloSizes().size() != numSplitAxes * 2) { |
| return emitError() << "halo sizes must be specified for all split axes."; |
| } |
| } |
| |
| return success(); |
| } |
| |
| void ShardingOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "sharding"); |
| } |
| |
| LogicalResult ShardingOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = ::getGridAndVerify(getOperation(), getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (mlir::ShapedType::isDynamicShape(grid->getShape()) && |
| getStaticShardedDimsOffsets().size() > 0) { |
| return emitError() << "sharded dims offsets are not allowed for " |
| "device grids with dynamic shape."; |
| } |
| |
| auto shardedDimsOffsets = getStaticShardedDimsOffsets(); |
| if (!shardedDimsOffsets.empty()) { |
| auto gridShape = grid.value().getShape(); |
| assert(ShapedType::isStaticShape(gridShape)); |
| uint64_t pos = 0; |
| for (auto [tensorAxis, innerSplitAxes] : llvm::enumerate(getSplitAxes())) { |
| if (!innerSplitAxes.empty()) { |
| int64_t numShards = 0, off = 0; |
| for (auto i : innerSplitAxes.asArrayRef()) { |
| numShards += gridShape[i]; |
| } |
| for (int64_t i = 0; i <= numShards; ++i) { |
| if (shardedDimsOffsets.size() <= pos + i) { |
| return emitError() << "sharded dims offsets has wrong size."; |
| } |
| if (ShapedType::isStatic(shardedDimsOffsets[pos + i])) { |
| if (shardedDimsOffsets[pos + i] < off) { |
| return emitError() |
| << "sharded dims offsets must be non-decreasing."; |
| } |
| off = shardedDimsOffsets[pos + i]; |
| } |
| } |
| pos += numShards + 1; |
| } |
| } |
| } |
| return success(); |
| } |
| |
| namespace { |
| // Sharding annotations "halo sizes" and "sharded dims offsets" |
| // are a mix of attributes and dynamic values. This canonicalization moves |
| // constant values to the respective attribute lists, minimizing the number |
| // of values. |
| // It also removes sharded_dims_sizes and halos if they are effectively "empty". |
| class NormalizeSharding final : public OpRewritePattern<ShardingOp> { |
| public: |
| using OpRewritePattern<ShardingOp>::OpRewritePattern; |
| |
| LogicalResult matchAndRewrite(ShardingOp op, |
| PatternRewriter &b) const override { |
| auto mixedHalos = |
| getMixedValues(op.getStaticHaloSizes(), op.getDynamicHaloSizes(), b); |
| auto mixedOffs = getMixedValues(op.getStaticShardedDimsOffsets(), |
| op.getDynamicShardedDimsOffsets(), b); |
| |
| // No constant operands were folded, just return; |
| bool modified = succeeded(foldDynamicIndexList(mixedHalos, true)) || |
| succeeded(foldDynamicIndexList(mixedOffs, true)); |
| |
| auto [staticHalos, dynamicHalos] = decomposeMixedValues(mixedHalos); |
| auto [staticOffs, dynamicOffs] = decomposeMixedValues(mixedOffs); |
| |
| if (dynamicHalos.empty() && !staticHalos.empty()) { |
| if (staticHalos[0] == 0 && llvm::all_equal(staticHalos)) { |
| staticHalos.clear(); |
| modified = true; |
| } |
| } |
| |
| // Remove sharded dims offsets if they are effectively the default values, |
| // e.g. if they define equi-distance between all neighboring shards. |
| // Requires static-only offsets. Compares the first distance as the |
| // difference between the first two offsets. Only if all consecutive |
| // distances are the same, the offsets are removed. |
| if (dynamicOffs.empty() && !staticOffs.empty()) { |
| assert(staticOffs.size() >= 2); |
| auto diff = staticOffs[1] - staticOffs[0]; |
| bool all_same = staticOffs.size() > 2; |
| for (auto i = 2u; i < staticOffs.size(); ++i) { |
| if (staticOffs[i] - staticOffs[i - 1] != diff) { |
| all_same = false; |
| break; |
| } |
| } |
| if (all_same) { |
| staticOffs.clear(); |
| modified = true; |
| } |
| } |
| |
| if (!modified) { |
| return failure(); |
| } |
| |
| op.setStaticHaloSizes(staticHalos); |
| op.getDynamicHaloSizesMutable().assign(dynamicHalos); |
| op.setStaticShardedDimsOffsets(staticOffs); |
| op.getDynamicShardedDimsOffsetsMutable().assign(dynamicOffs); |
| |
| return success(); |
| } |
| }; |
| } // namespace |
| |
| void ShardingOp::getCanonicalizationPatterns(mlir::RewritePatternSet &results, |
| mlir::MLIRContext *context) { |
| results.add<NormalizeSharding>(context); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // Sharding |
| //===----------------------------------------------------------------------===// |
| |
| bool Sharding::equalSplitAxes(const Sharding &rhs) const { |
| if (getGrid() != rhs.getGrid()) { |
| return false; |
| } |
| |
| auto minSize = std::min(getSplitAxes().size(), rhs.getSplitAxes().size()); |
| if (!llvm::equal(llvm::make_range(getSplitAxes().begin(), |
| getSplitAxes().begin() + minSize), |
| llvm::make_range(rhs.getSplitAxes().begin(), |
| rhs.getSplitAxes().begin() + minSize))) { |
| return false; |
| } |
| |
| return llvm::all_of(llvm::drop_begin(getSplitAxes(), minSize), |
| std::mem_fn(&GridAxesAttr::empty)) && |
| llvm::all_of(llvm::drop_begin(rhs.getSplitAxes(), minSize), |
| std::mem_fn(&GridAxesAttr::empty)); |
| } |
| |
| bool Sharding::equalHaloAndShardSizes(const Sharding &rhs) const { |
| return equalShardSizes(rhs) && equalHaloSizes(rhs); |
| } |
| |
| bool Sharding::equalShardSizes(const Sharding &rhs) const { |
| if (rhs.getStaticShardedDimsOffsets().size() != |
| getStaticShardedDimsOffsets().size() || |
| !llvm::equal(getStaticShardedDimsOffsets(), |
| rhs.getStaticShardedDimsOffsets())) { |
| return false; |
| } |
| if (rhs.getDynamicShardedDimsOffsets().size() != |
| getDynamicShardedDimsOffsets().size() || |
| !llvm::equal(getDynamicShardedDimsOffsets(), |
| rhs.getDynamicShardedDimsOffsets())) { |
| return false; |
| } |
| return true; |
| } |
| |
| bool Sharding::equalHaloSizes(const Sharding &rhs) const { |
| if (rhs.getStaticHaloSizes().size() != getStaticHaloSizes().size() || |
| !llvm::equal(getStaticHaloSizes(), rhs.getStaticHaloSizes())) { |
| return false; |
| } |
| if (rhs.getDynamicHaloSizes().size() != getDynamicHaloSizes().size() || |
| !llvm::equal(getDynamicHaloSizes(), rhs.getDynamicHaloSizes())) { |
| return false; |
| } |
| return true; |
| } |
| |
| bool Sharding::operator==(Value rhs) const { |
| return equalSplitAxes(rhs) && equalHaloAndShardSizes(rhs); |
| } |
| |
| bool Sharding::operator!=(Value rhs) const { return !(*this == rhs); } |
| |
| bool Sharding::operator==(const Sharding &rhs) const { |
| return equalSplitAxes(rhs) && equalHaloAndShardSizes(rhs); |
| } |
| |
| bool Sharding::operator!=(const Sharding &rhs) const { return !(*this == rhs); } |
| |
| Sharding::Sharding(::mlir::FlatSymbolRefAttr grid_) : grid(grid_) {} |
| |
| Sharding::Sharding(Value rhs) { |
| auto shardingOp = rhs.getDefiningOp<ShardingOp>(); |
| assert(shardingOp && "expected sharding op"); |
| auto splitAxes = shardingOp.getSplitAxes().getAxes(); |
| // If splitAxes are empty, use "empty" constructor. |
| if (splitAxes.empty()) { |
| *this = Sharding(shardingOp.getGridAttr()); |
| return; |
| } |
| *this = |
| get(shardingOp.getGridAttr(), splitAxes, shardingOp.getStaticHaloSizes(), |
| shardingOp.getStaticShardedDimsOffsets(), |
| SmallVector<Value>(shardingOp.getDynamicHaloSizes()), |
| SmallVector<Value>(shardingOp.getDynamicShardedDimsOffsets())); |
| } |
| |
| Sharding Sharding::get(::mlir::FlatSymbolRefAttr grid_, |
| ArrayRef<GridAxesAttr> split_axes_, |
| ArrayRef<int64_t> static_halo_sizes_, |
| ArrayRef<int64_t> static_sharded_dims_offsets_, |
| ArrayRef<Value> dynamic_halo_sizes_, |
| ArrayRef<Value> dynamic_sharded_dims_offsets_) { |
| Sharding res(grid_); |
| if (split_axes_.empty()) { |
| return res; |
| } |
| |
| res.split_axes.resize(split_axes_.size()); |
| for (auto [i, axis] : llvm::enumerate(split_axes_)) { |
| res.split_axes[i] = |
| GridAxesAttr::get(grid_.getContext(), axis.asArrayRef()); |
| } |
| |
| auto clone = [](const auto src, auto &dst) { |
| dst.resize(src.size()); |
| llvm::copy(src, dst.begin()); |
| }; |
| |
| clone(static_halo_sizes_, res.static_halo_sizes); |
| clone(static_sharded_dims_offsets_, res.static_sharded_dims_offsets); |
| clone(dynamic_halo_sizes_, res.dynamic_halo_sizes); |
| clone(dynamic_sharded_dims_offsets_, res.dynamic_sharded_dims_offsets); |
| |
| return res; |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.shard_shape |
| //===----------------------------------------------------------------------===// |
| |
| void ShardShapeOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult()[0], "shard_shape"); |
| } |
| |
| void ShardShapeOp::build(::mlir::OpBuilder &odsBuilder, |
| ::mlir::OperationState &odsState, |
| ::llvm::ArrayRef<int64_t> dims, |
| ArrayRef<Value> dims_dyn, ::mlir::Value sharding, |
| ::mlir::ValueRange device) { |
| SmallVector<mlir::Type> resType(dims.size(), odsBuilder.getIndexType()); |
| build(odsBuilder, odsState, resType, dims, dims_dyn, sharding, |
| SmallVector<int64_t>(device.size(), ShapedType::kDynamic), device); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.shard op |
| //===----------------------------------------------------------------------===// |
| |
| void ShardOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "sharding_annotated"); |
| } |
| |
| namespace { |
| // Determine if the given ShardOp is a duplicate of another ShardOp |
| // on the same value. This can happen if constant values are sharded. |
| class FoldDuplicateShardOp final : public OpRewritePattern<ShardOp> { |
| public: |
| using OpRewritePattern<ShardOp>::OpRewritePattern; |
| |
| LogicalResult matchAndRewrite(ShardOp op, PatternRewriter &b) const override { |
| // Get the use-list of the value being sharded and check if it has more than |
| // one use. |
| Value value = op.getSrc(); |
| if (value.hasOneUse() || value.getDefiningOp<ShardOp>()) { |
| return failure(); |
| } |
| |
| // Iterate through the uses of the value to find a duplicate ShardOp. |
| for (auto &use : value.getUses()) { |
| if (use.getOwner() != op.getOperation()) { |
| auto otherOp = dyn_cast<ShardOp>(use.getOwner()); |
| if (!otherOp || !otherOp->isBeforeInBlock(op)) { |
| return failure(); |
| } |
| // Create a Sharding object for the current and the other ShardOp |
| // If the two are equal replace current op with the other op. |
| Sharding currentSharding(op.getSharding()); |
| Sharding otherSharding(otherOp.getSharding()); |
| if (currentSharding == otherSharding) { |
| b.replaceAllUsesWith(op.getResult(), otherOp.getResult()); |
| b.eraseOp(op.getOperation()); |
| } else { |
| // use the other sharding as input for op |
| op.getSrcMutable().assign(otherOp.getResult()); |
| } |
| return success(); |
| } |
| } |
| |
| return failure(); |
| } |
| }; |
| } // namespace |
| |
| void ShardOp::getCanonicalizationPatterns(mlir::RewritePatternSet &results, |
| mlir::MLIRContext *context) { |
| results.add<FoldDuplicateShardOp>(context); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.process_multi_index op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| ProcessMultiIndexOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = ::getGridAndVerify(getOperation(), getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyGridAxes(getLoc(), getAxes(), grid.value()))) { |
| return failure(); |
| } |
| |
| size_t expectedResultsCount = |
| getAxes().empty() ? grid->getRank() : getAxes().size(); |
| if (getResult().size() != expectedResultsCount) { |
| return emitError() << "Unexpected number of results " << getResult().size() |
| << ". Expected " << expectedResultsCount << "."; |
| } |
| |
| return success(); |
| } |
| |
| void ProcessMultiIndexOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| GridOp grid) { |
| build(odsBuilder, odsState, |
| SmallVector<Type>(grid.getRank(), odsBuilder.getIndexType()), |
| grid.getSymName(), ArrayRef<GridAxis>()); |
| } |
| |
| void ProcessMultiIndexOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| StringRef grid, ArrayRef<GridAxis> axes) { |
| build(odsBuilder, odsState, |
| SmallVector<Type>(axes.size(), odsBuilder.getIndexType()), grid, |
| GridAxesAttr::get(odsBuilder.getContext(), axes)); |
| } |
| |
| void ProcessMultiIndexOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResults()[0], "proc_linear_idx"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.process_linear_index op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| ProcessLinearIndexOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = ::getGridAndVerify(getOperation(), getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| return success(); |
| } |
| |
| void ProcessLinearIndexOp::build(OpBuilder &odsBuilder, |
| OperationState &odsState, GridOp grid) { |
| build(odsBuilder, odsState, grid.getSymName()); |
| } |
| |
| void ProcessLinearIndexOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "proc_linear_idx"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.neighbors_linear_indices op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| NeighborsLinearIndicesOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = ::getGridAndVerify(getOperation(), getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| return success(); |
| } |
| |
| void NeighborsLinearIndicesOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getNeighborDown(), "down_linear_idx"); |
| setNameFn(getNeighborUp(), "up_linear_idx"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // collective communication ops |
| //===----------------------------------------------------------------------===// |
| |
| namespace { |
| |
| template <typename Op> |
| struct EmptyGridAxesCanonicalizationPattern : OpRewritePattern<Op> { |
| using OpRewritePattern<Op>::OpRewritePattern; |
| LogicalResult matchAndRewrite(Op op, |
| PatternRewriter &rewriter) const override { |
| auto gridAxes = op.getGridAxes(); |
| if (!gridAxes.empty()) { |
| return failure(); |
| } |
| if (op.getInput().getType() != op.getResult().getType()) { |
| return failure(); |
| } |
| |
| rewriter.replaceAllUsesWith(op.getResult(), op.getInput()); |
| rewriter.eraseOp(op.getOperation()); |
| return success(); |
| } |
| }; |
| |
| } // namespace |
| |
| static LogicalResult verifyInGroupDevice(Location loc, StringRef deviceName, |
| ArrayRef<int64_t> device, |
| Operation::operand_range deviceDynamic, |
| ArrayRef<GridAxis> gridAxes, |
| ArrayRef<int64_t> gridShape) { |
| if (device.size() != gridAxes.size()) { |
| return emitError(loc) << "In-group device \"" << deviceName |
| << "\" has unexpected multi-index size " |
| << device.size() << ". Expected " << gridAxes.size() |
| << "."; |
| } |
| |
| for (size_t i = 0; i < device.size(); ++i) { |
| if (ShapedType::isStatic(device[i]) && |
| ShapedType::isStatic(gridShape[gridAxes[i]]) && |
| gridShape[gridAxes[i]] <= device[i]) { |
| return emitError(loc) |
| << "Out of bounds coordinate " << i << " for in-group device \"" |
| << deviceName << "\"." |
| << " Got " << device[i] << ", but expected value in the range [0, " |
| << (gridShape[gridAxes[i]] - 1) << "]."; |
| } |
| } |
| return success(); |
| } |
| |
| static LogicalResult verifyDimensionCompatibility(Location loc, |
| int64_t expectedDimSize, |
| int64_t resultDimSize, |
| int64_t resultAxis) { |
| if (ShapedType::isStatic(resultDimSize) && expectedDimSize != resultDimSize) { |
| return emitError(loc) << "Dimension size mismatch for result axis " |
| << resultAxis << ". Expected " |
| << (ShapedType::isDynamic(expectedDimSize) |
| ? Twine("dynamic") |
| : Twine(expectedDimSize)) |
| << ", but got " << resultDimSize << "."; |
| } |
| |
| return success(); |
| } |
| |
| static LogicalResult verifyGatherOperandAndResultShape( |
| Value operand, Value result, int64_t gatherAxis, |
| ArrayRef<GridAxis> gridAxes, ArrayRef<int64_t> gridShape) { |
| auto resultRank = cast<ShapedType>(result.getType()).getRank(); |
| if (gatherAxis < 0 || gatherAxis >= resultRank) { |
| return emitError(result.getLoc()) |
| << "Gather axis " << gatherAxis << " is out of bounds [0, " |
| << resultRank << ")."; |
| } |
| |
| ShapedType operandType = cast<ShapedType>(operand.getType()); |
| ShapedType resultType = cast<ShapedType>(result.getType()); |
| auto deviceGroupSize = |
| DimensionSize(collectiveProcessGroupSize(gridAxes, gridShape)); |
| for (int64_t axis = 0; axis < operandType.getRank(); ++axis) { |
| auto operandDimSize = DimensionSize(operandType.getDimSize(axis)); |
| auto resultDimSize = DimensionSize(resultType.getDimSize(axis)); |
| auto expectedResultDimSize = |
| axis == gatherAxis ? deviceGroupSize * operandDimSize : operandDimSize; |
| if (failed(verifyDimensionCompatibility( |
| result.getLoc(), expectedResultDimSize, resultDimSize, axis))) { |
| return failure(); |
| } |
| } |
| return success(); |
| } |
| |
| static LogicalResult verifyAllToAllOperandAndResultShape( |
| Value operand, Value result, int64_t splitAxis, int64_t concatAxis, |
| ArrayRef<GridAxis> gridAxes, ArrayRef<int64_t> gridShape) { |
| ShapedType operandType = cast<ShapedType>(operand.getType()); |
| ShapedType resultType = cast<ShapedType>(result.getType()); |
| for (int64_t axis = 0; axis < operandType.getRank(); ++axis) { |
| if ((axis != splitAxis && axis != concatAxis) || splitAxis == concatAxis) { |
| if (failed(verifyDimensionCompatibility( |
| result.getLoc(), operandType.getDimSize(axis), |
| resultType.getDimSize(axis), axis))) { |
| return failure(); |
| } |
| } |
| } |
| |
| if (splitAxis == concatAxis) { |
| return success(); |
| } |
| |
| auto deviceGroupSize = |
| DimensionSize(collectiveProcessGroupSize(gridAxes, gridShape)); |
| auto operandConcatDimSize = DimensionSize(operandType.getDimSize(concatAxis)); |
| auto operandSplitDimSize = DimensionSize(operandType.getDimSize(splitAxis)); |
| DimensionSize expectedResultConcatDimSize = |
| operandConcatDimSize * deviceGroupSize; |
| DimensionSize expectedResultSplitDimSize = |
| operandSplitDimSize / deviceGroupSize; |
| if (!expectedResultSplitDimSize.isDynamic() && |
| int64_t(operandSplitDimSize) % int64_t(deviceGroupSize) != 0) { |
| expectedResultSplitDimSize = DimensionSize::dynamic(); |
| } |
| if (failed(verifyDimensionCompatibility( |
| result.getLoc(), expectedResultConcatDimSize.value(), |
| resultType.getDimSize(concatAxis), concatAxis))) { |
| return failure(); |
| } |
| if (failed(verifyDimensionCompatibility( |
| result.getLoc(), expectedResultSplitDimSize.value(), |
| resultType.getDimSize(splitAxis), splitAxis))) { |
| return failure(); |
| } |
| |
| return success(); |
| } |
| |
| static LogicalResult verifyScatterOrSliceOperandAndResultShape( |
| Value operand, Value result, int64_t tensorAxis, |
| ArrayRef<GridAxis> gridAxes, ArrayRef<int64_t> gridShape) { |
| ShapedType operandType = cast<ShapedType>(operand.getType()); |
| ShapedType resultType = cast<ShapedType>(result.getType()); |
| for (int64_t axis = 0; axis < operandType.getRank(); ++axis) { |
| if (axis != tensorAxis) { |
| if (failed(verifyDimensionCompatibility( |
| result.getLoc(), operandType.getDimSize(axis), |
| resultType.getDimSize(axis), axis))) { |
| return failure(); |
| } |
| } |
| } |
| |
| auto deviceGroupSize = |
| DimensionSize(collectiveProcessGroupSize(gridAxes, gridShape)); |
| auto operandScatterDimSize = |
| DimensionSize(operandType.getDimSize(tensorAxis)); |
| if (!operandScatterDimSize.isDynamic() && !deviceGroupSize.isDynamic() && |
| int64_t(operandScatterDimSize) % int64_t(deviceGroupSize) != 0) { |
| return emitError(result.getLoc()) |
| << "Operand dimension size " << int64_t(operandScatterDimSize) |
| << " is not divisible by collective device group size " |
| << int64_t(deviceGroupSize) << " for tensor axis " << tensorAxis |
| << "."; |
| } |
| DimensionSize expectedResultTensorDimSize = |
| operandScatterDimSize / deviceGroupSize; |
| if (failed(verifyDimensionCompatibility( |
| result.getLoc(), expectedResultTensorDimSize.value(), |
| resultType.getDimSize(tensorAxis), tensorAxis))) { |
| return failure(); |
| } |
| |
| return success(); |
| } |
| |
| static RankedTensorType sliceResultType(Type operandType, GridOp grid, |
| ArrayRef<GridAxis> gridAxes, |
| int64_t sliceAxis) { |
| RankedTensorType operandRankedTensorType = |
| cast<RankedTensorType>(operandType); |
| DimensionSize operandSliceAxisSize = |
| operandRankedTensorType.getShape()[sliceAxis]; |
| SmallVector<int64_t> resultShape = |
| llvm::to_vector(operandRankedTensorType.getShape()); |
| |
| resultShape[sliceAxis] = |
| operandSliceAxisSize / |
| DimensionSize(collectiveProcessGroupSize(gridAxes, grid)); |
| return operandRankedTensorType.clone(resultShape); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.all_gather op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| AllGatherOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| auto gatherAxis = getGatherAxis().getSExtValue(); |
| return verifyGatherOperandAndResultShape(getOperand(), getResult(), |
| gatherAxis, getGridAxes(), |
| grid.value().getShape()); |
| } |
| |
| void AllGatherOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<AllGatherOp>>(context); |
| } |
| |
| void AllGatherOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "all_gather"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.all_reduce op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| AllReduceOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| return getGridAndVerifyAxes(*this, symbolTable); |
| } |
| |
| void AllReduceOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<AllReduceOp>>(context); |
| } |
| |
| void AllReduceOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| Value input, StringRef grid, |
| ArrayRef<GridAxis> gridAxes, ReductionKind reduction) { |
| build(odsBuilder, odsState, input.getType(), grid, gridAxes, input, |
| reduction); |
| } |
| |
| void AllReduceOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "all_reduce"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.all_slice op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult AllSliceOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| return verifyScatterOrSliceOperandAndResultShape( |
| getOperand(), getResult(), getSliceAxis().getSExtValue(), getGridAxes(), |
| grid.value().getShape()); |
| } |
| |
| void AllSliceOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<AllSliceOp>>(context); |
| } |
| |
| void AllSliceOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| Value input, GridOp grid, ArrayRef<GridAxis> gridAxes, |
| int64_t sliceAxis) { |
| Type resultType = sliceResultType(input.getType(), grid, gridAxes, sliceAxis); |
| build(odsBuilder, odsState, resultType, input, grid.getSymName(), gridAxes, |
| sliceAxis); |
| } |
| |
| void AllSliceOp::build(OpBuilder &odsBuilder, OperationState &odsState, |
| Type resultType, Value input, StringRef grid, |
| ArrayRef<GridAxis> gridAxes, int64_t sliceAxis) { |
| build(odsBuilder, odsState, resultType, grid, gridAxes, input, |
| APInt(sizeof(sliceAxis) * CHAR_BIT, sliceAxis)); |
| } |
| |
| void AllSliceOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "all_slice"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.all_to_all op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult AllToAllOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| |
| return verifyAllToAllOperandAndResultShape( |
| getOperand(), getResult(), getSplitAxis().getSExtValue(), |
| getConcatAxis().getSExtValue(), getGridAxes(), grid.value().getShape()); |
| } |
| |
| void AllToAllOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<AllToAllOp>>(context); |
| } |
| |
| void AllToAllOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "all_to_all"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.broadcast op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| BroadcastOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyInGroupDevice(getLoc(), getRootAttrName(), getRoot(), |
| getRootDynamic(), getGridAxes(), |
| grid.value().getShape()))) { |
| return failure(); |
| } |
| |
| return success(); |
| } |
| |
| void BroadcastOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<BroadcastOp>>(context); |
| } |
| |
| void BroadcastOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "broadcast"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.gather op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult GatherOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyInGroupDevice(getLoc(), getRootAttrName(), getRoot(), |
| getRootDynamic(), getGridAxes(), |
| grid.value().getShape()))) { |
| return failure(); |
| } |
| |
| auto gatherAxis = getGatherAxis().getSExtValue(); |
| return verifyGatherOperandAndResultShape(getInput(), getResult(), gatherAxis, |
| getGridAxes(), |
| grid.value().getShape()); |
| } |
| |
| void GatherOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<GatherOp>>(context); |
| } |
| |
| void GatherOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "gather"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.recv op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult RecvOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (getSource() && |
| failed(verifyInGroupDevice(getLoc(), getSourceAttrName(), |
| getSource().value(), getSourceDynamic(), |
| getGridAxes(), grid.value().getShape()))) { |
| return failure(); |
| } |
| return success(); |
| } |
| |
| void RecvOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<RecvOp>>(context); |
| } |
| |
| void RecvOp::getAsmResultNames(function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "recv"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.reduce op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult ReduceOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyInGroupDevice(getLoc(), getRootAttrName(), getRoot(), |
| getRootDynamic(), getGridAxes(), |
| grid.value().getShape()))) { |
| return failure(); |
| } |
| |
| return success(); |
| } |
| |
| void ReduceOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<ReduceOp>>(context); |
| } |
| |
| void ReduceOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "reduce"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.reduce_scatter op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| ReduceScatterOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| |
| return verifyScatterOrSliceOperandAndResultShape( |
| getOperand(), getResult(), getScatterAxis().getSExtValue(), getGridAxes(), |
| grid.value().getShape()); |
| } |
| |
| void ReduceScatterOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<ReduceScatterOp>>(context); |
| } |
| |
| void ReduceScatterOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "reduce_scatter"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.scatter op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult ScatterOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyInGroupDevice(getLoc(), getRootAttrName(), getRoot(), |
| getRootDynamic(), getGridAxes(), |
| grid.value().getShape()))) { |
| return failure(); |
| } |
| |
| auto scatterAxis = getScatterAxis().getSExtValue(); |
| return verifyScatterOrSliceOperandAndResultShape(getInput(), getResult(), |
| scatterAxis, getGridAxes(), |
| grid.value().getShape()); |
| } |
| |
| void ScatterOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<ScatterOp>>(context); |
| } |
| |
| void ScatterOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "scatter"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.send op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult SendOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| if (failed(verifyInGroupDevice(getLoc(), getDestinationAttrName(), |
| getDestination(), getDestinationDynamic(), |
| getGridAxes(), grid.value().getShape()))) { |
| return failure(); |
| } |
| return success(); |
| } |
| |
| void SendOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| patterns.add<EmptyGridAxesCanonicalizationPattern<SendOp>>(context); |
| } |
| |
| void SendOp::getAsmResultNames(function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "send"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.shift op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult ShiftOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerifyAxes(*this, symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| |
| auto gridAxes = getGridAxes(); |
| auto shiftAxis = getShiftAxis().getZExtValue(); |
| if (!llvm::is_contained(gridAxes, shiftAxis)) { |
| return emitError() << "Invalid shift axis " << shiftAxis |
| << ". It must be one of the grouping grid axes."; |
| } |
| |
| return success(); |
| } |
| |
| void ShiftOp::getCanonicalizationPatterns(RewritePatternSet &patterns, |
| MLIRContext *context) { |
| // TODO: remove op when offset is 0 or if it is a rotate with and |
| // offset % shift_axis_grid_dim_size == 0. |
| } |
| |
| void ShiftOp::getAsmResultNames( |
| function_ref<void(Value, StringRef)> setNameFn) { |
| setNameFn(getResult(), "shift"); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // shard.update_halo op |
| //===----------------------------------------------------------------------===// |
| |
| LogicalResult |
| UpdateHaloOp::verifySymbolUses(SymbolTableCollection &symbolTable) { |
| auto grid = getGridAndVerify(getOperation(), getGridAttr(), symbolTable); |
| if (failed(grid)) { |
| return failure(); |
| } |
| |
| return success(); |
| } |
| |
| //===----------------------------------------------------------------------===// |
| // TableGen'd op method definitions |
| //===----------------------------------------------------------------------===// |
| |
| #define GET_OP_CLASSES |
| #include "mlir/Dialect/Shard/IR/ShardOps.cpp.inc" |
| |
| #define GET_ATTRDEF_CLASSES |
| #include "mlir/Dialect/Shard/IR/ShardAttributes.cpp.inc" |
| |
| #define GET_TYPEDEF_CLASSES |
| #include "mlir/Dialect/Shard/IR/ShardTypes.cpp.inc" |
| |
| #include "mlir/Dialect/Shard/IR/ShardEnums.cpp.inc" |
