[mlir][hlo] Refactor rank specialization to allow an arbitrary number of inputs
This actually simplifies the code a bit. PiperOrigin-RevId: 358201038
This commit is contained in:
Benjamin Kramer
TensorFlow MLIR Team
parent
ca4034b56e
commit
b42def4612
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@ -202,6 +202,149 @@ struct ConvertUnrankedScalarDynamicBroadcastBinaryOp
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}
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};
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template <typename ChloOpTy, typename HloOpTy>
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struct ConvertUnrankedDynamicBroadcastOpHelper {
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// Returns the dynamic result of checking the given value is effectively a
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// scalar shape (i.e. the number of elements is 1).
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static Value GreaterRankIsN(OpBuilder &builder, Location loc,
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Value actual_rank, int targeted_rank) {
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return builder.create<CmpIOp>(
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loc, CmpIPredicate::eq, actual_rank,
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builder.create<ConstantIndexOp>(loc, targeted_rank));
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}
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static scf::IfOp createIfOpForRankSpecializedBroadcastAndOp(
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OpBuilder &builder, ChloOpTy op, Value actual_rank, int targeted_rank) {
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// Create the if block to place the current specialized logic in.
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Value greater_rank_is_n =
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GreaterRankIsN(builder, op.getLoc(), actual_rank, targeted_rank);
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return builder.create<scf::IfOp>(op.getLoc(), op.getResult().getType(),
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greater_rank_is_n, true);
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}
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static Value createBroadcastToKnownRank(OpBuilder &builder, ChloOpTy op,
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Value value, int targeted_rank) {
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auto loc = op.getLoc();
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Value shape = builder.create<shape::ShapeOfOp>(loc, value);
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SmallVector<int64_t, 6> ranked_shape(targeted_rank, 1);
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auto unknown_rank_extent_tensor_type = RankedTensorType::get(
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{RankedTensorType::kDynamicSize}, builder.getIndexType());
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auto known_rank_extent_tensor_type =
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RankedTensorType::get({targeted_rank}, builder.getIndexType());
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Value ranked_shape_val = builder.create<shape::ConstShapeOp>(
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loc, known_rank_extent_tensor_type,
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mlir::DenseIntElementsAttr::get(known_rank_extent_tensor_type,
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ranked_shape));
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Value extended_value = builder.create<shape::BroadcastOp>(
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loc, unknown_rank_extent_tensor_type, shape, ranked_shape_val, nullptr);
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return builder.create<tensor::CastOp>(loc, known_rank_extent_tensor_type,
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extended_value);
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}
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// Create the if statement and code for a broadcasting op with a result of a
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// given rank.
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static void createRankSpecializedBroadcastAndOp(OpBuilder &if_builder,
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ChloOpTy op,
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ValueRange operands,
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int targeted_rank) {
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auto loc = op.getLoc();
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SmallVector<Value, 2> reshaped_operands;
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auto dynamic_dimensions = llvm::SmallVector<int64_t, 6>(
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targeted_rank, RankedTensorType::kDynamicSize);
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for (Value operand : operands) {
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// Handle shape broadcasting and inference.
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Value extended_operand_casted =
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createBroadcastToKnownRank(if_builder, op, operand, targeted_rank);
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// 1. Reshape operands to the given rank (with the same number of
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// elements)
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// 2. Compute the ranked-broadcasted ChloOp (which will assert that the
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// ops
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// can be broadcasted and do the actual broadcasting)
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// 3. Type erase the output back to unranked
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auto reshaped_type = RankedTensorType::get(
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dynamic_dimensions,
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operand.getType().template dyn_cast<TensorType>().getElementType());
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Value reshaped_operand = if_builder.create<mhlo::DynamicReshapeOp>(
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loc, reshaped_type, operand, extended_operand_casted);
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reshaped_operands.push_back(reshaped_operand);
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}
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auto result_element_type = op.getResult()
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.getType()
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.template dyn_cast<TensorType>()
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.getElementType();
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auto result_type =
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RankedTensorType::get(dynamic_dimensions, result_element_type);
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Value result = if_builder.create<ChloOpTy>(
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loc, ArrayRef<Type>{result_type}, reshaped_operands, op.getAttrs());
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Value reshaped_result = if_builder.create<tensor::CastOp>(
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loc, UnrankedTensorType::get(result_element_type), result);
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if_builder.create<scf::YieldOp>(loc, reshaped_result);
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}
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// Iterates over the desired ranks to be specialized and generates the code
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// snippet for each case.
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static Value HandleBroadcastAndOp(OpBuilder &rewriter, ChloOpTy op,
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ValueRange operands) {
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auto loc = op.getLoc();
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// Find the larger rank of the operands.
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auto extent_tensor_type = RankedTensorType::get({ShapedType::kDynamicSize},
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rewriter.getIndexType());
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Value greater_rank;
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for (Value operand : operands) {
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Value shape =
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rewriter.create<shape::ShapeOfOp>(loc, extent_tensor_type, operand);
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Value rank =
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rewriter.create<shape::RankOp>(loc, rewriter.getIndexType(), shape);
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if (!greater_rank) {
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greater_rank = rank;
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} else {
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Value greater_rank_compare = rewriter.create<CmpIOp>(
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loc, CmpIPredicate::sgt, greater_rank, rank);
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greater_rank = rewriter.create<SelectOp>(loc, greater_rank_compare,
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greater_rank, rank);
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}
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}
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// Generate a list of nested if/else statements to handle rank
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// specializations from 1 to `kMaxRankSpecialization`.
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scf::IfOp if_op = createIfOpForRankSpecializedBroadcastAndOp(
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rewriter, op, greater_rank, 1);
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OpBuilder if_builder = if_op.getThenBodyBuilder(rewriter.getListener());
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createRankSpecializedBroadcastAndOp(if_builder, op, operands, 1);
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// Put each subsequent rank specialization inside the else statement of the
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// previous one.
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OpBuilder else_builder = if_op.getElseBodyBuilder(rewriter.getListener());
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constexpr int kMaxRankSpecialization = 6;
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for (int i = 2; i < kMaxRankSpecialization; i++) {
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auto inner_if = createIfOpForRankSpecializedBroadcastAndOp(
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else_builder, op, greater_rank, i);
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if_builder = inner_if.getThenBodyBuilder(rewriter.getListener());
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createRankSpecializedBroadcastAndOp(if_builder, op, operands, i);
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else_builder.create<scf::YieldOp>(loc, inner_if.getResult(0));
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else_builder = inner_if.getElseBodyBuilder(rewriter.getListener());
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}
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// Fire an assertion if none of the rank specializations applied (one of
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// the ranks was greater than `kMaxRankSpecialization`).
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else_builder.create<AssertOp>(
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loc,
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GreaterRankIsN(else_builder, op.getLoc(), greater_rank,
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kMaxRankSpecialization),
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"Input for dynamic binary op lowering was of a rank greater than " +
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std::to_string(kMaxRankSpecialization));
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// Add the rank 6 specialization to the innermost else block.
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createRankSpecializedBroadcastAndOp(else_builder, op, operands,
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kMaxRankSpecialization);
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// Return the result of the outermost if statement.
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return if_op.getResult(0);
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}
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};
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// Handles lowering of the following pattern to patterns that will be further
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// matched by other patterns until they result in LHLO:
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// %result = "chlo.op"(%lhs, %rhs) : (<*xTy>, <*xTy>) -> <*xTy>
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@ -298,7 +441,9 @@ struct ConvertUnrankedDynamicBroadcastBinaryOp
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OpBuilder if_neq_shapes_builder =
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if_eq_shapes_op.getElseBodyBuilder(rewriter.getListener());
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if_neq_shapes_builder.create<scf::YieldOp>(
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loc, HandleBroadcastAndOp(if_neq_shapes_builder, op, lhs, rhs));
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loc, ConvertUnrankedDynamicBroadcastOpHelper<
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ChloOpTy, HloOpTy>::HandleBroadcastAndOp(if_neq_shapes_builder,
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op, {lhs, rhs}));
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rewriter.replaceOp(op, {if_op.getResult(0)});
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return success();
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@ -318,23 +463,6 @@ struct ConvertUnrankedDynamicBroadcastBinaryOp
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rewriter.create<ConstantIndexOp>(loc, 1));
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}
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Value GreaterRankIsN(OpBuilder &builder, Location loc, Value actual_rank,
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int targeted_rank) const {
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return builder.create<CmpIOp>(
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loc, CmpIPredicate::eq, actual_rank,
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builder.create<ConstantIndexOp>(loc, targeted_rank));
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}
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scf::IfOp createIfOpForRankSpecializedBroadcastAndOp(
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OpBuilder &builder, ChloOpTy op, Value actual_rank,
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int targeted_rank) const {
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// Create the if block to place the current specialized logic in.
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Value greater_rank_is_n =
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GreaterRankIsN(builder, op.getLoc(), actual_rank, targeted_rank);
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return builder.create<scf::IfOp>(op.getLoc(), op.getResult().getType(),
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greater_rank_is_n, true);
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}
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Value extendToBroadcastShape(OpBuilder &builder, Location loc, Value value,
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Value shape_of_lhs, Value shape_of_rhs) const {
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auto unknown_rank_extent_tensor_type = RankedTensorType::get(
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@ -345,122 +473,6 @@ struct ConvertUnrankedDynamicBroadcastBinaryOp
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return builder.create<mhlo::DynamicReshapeOp>(loc, value.getType(), value,
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broadcast_shape);
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}
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Value createBroadcastToKnownRank(OpBuilder &builder, ChloOpTy op, Value value,
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int targeted_rank) const {
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auto loc = op.getLoc();
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Value shape = builder.create<shape::ShapeOfOp>(loc, value);
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SmallVector<int64_t, 6> ranked_shape(targeted_rank, 1);
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auto unknown_rank_extent_tensor_type = RankedTensorType::get(
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{RankedTensorType::kDynamicSize}, builder.getIndexType());
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auto known_rank_extent_tensor_type =
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RankedTensorType::get({targeted_rank}, builder.getIndexType());
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Value ranked_shape_val = builder.create<shape::ConstShapeOp>(
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loc, known_rank_extent_tensor_type,
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mlir::DenseIntElementsAttr::get(known_rank_extent_tensor_type,
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ranked_shape));
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Value extended_value = builder.create<shape::BroadcastOp>(
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loc, unknown_rank_extent_tensor_type, shape, ranked_shape_val, nullptr);
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return builder.create<tensor::CastOp>(loc, known_rank_extent_tensor_type,
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extended_value);
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}
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// Create the if statement and code for a broadcasting op with a result of a
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// given rank.
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void createRankSpecializedBroadcastAndOp(OpBuilder &if_builder, ChloOpTy op,
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Value lhs, Value rhs,
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int targeted_rank) const {
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auto loc = op.getLoc();
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// Handle shape broadcasting and inference.
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Value extended_lhs_casted =
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createBroadcastToKnownRank(if_builder, op, lhs, targeted_rank);
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Value extended_rhs_casted =
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createBroadcastToKnownRank(if_builder, op, rhs, targeted_rank);
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auto dynamic_dimensions = llvm::SmallVector<int64_t, 6>(
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targeted_rank, RankedTensorType::kDynamicSize);
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auto reshaped_type = RankedTensorType::get(
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dynamic_dimensions,
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lhs.getType().template dyn_cast<TensorType>().getElementType());
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// 1. Reshape operands to the given rank (with the same number of elements)
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// 2. Compute the ranked-broadcasted ChloOp (which will assert that the ops
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// can be broadcasted and do the actual broadcasting)
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// 3. Type erase the output back to unranked
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Value reshaped_lhs = if_builder.create<mhlo::DynamicReshapeOp>(
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loc, reshaped_type, lhs, extended_lhs_casted);
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Value reshaped_rhs = if_builder.create<mhlo::DynamicReshapeOp>(
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loc, reshaped_type, rhs, extended_rhs_casted);
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auto result_element_type = op.getResult()
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.getType()
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.template dyn_cast<TensorType>()
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.getElementType();
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auto result_type =
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RankedTensorType::get(dynamic_dimensions, result_element_type);
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Value result = if_builder.create<ChloOpTy>(
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loc, ArrayRef<Type>{result_type},
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ArrayRef<Value>{reshaped_lhs, reshaped_rhs}, op.getAttrs());
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Value reshaped_result = if_builder.create<tensor::CastOp>(
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loc, UnrankedTensorType::get(result_element_type), result);
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if_builder.create<scf::YieldOp>(loc, reshaped_result);
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}
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// Iterates over the desired ranks to be specialized and generates the code
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// snippet for each case.
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Value HandleBroadcastAndOp(OpBuilder &rewriter, ChloOpTy op, Value lhs,
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Value rhs) const {
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auto loc = op.getLoc();
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// Find the larger rank of the 2 operands.
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auto extent_tensor_type = RankedTensorType::get({ShapedType::kDynamicSize},
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rewriter.getIndexType());
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Value lhs_shape =
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rewriter.create<shape::ShapeOfOp>(loc, extent_tensor_type, lhs);
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Value rhs_shape =
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rewriter.create<shape::ShapeOfOp>(loc, extent_tensor_type, rhs);
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Value lhs_rank =
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rewriter.create<shape::RankOp>(loc, rewriter.getIndexType(), lhs_shape);
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Value rhs_rank =
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rewriter.create<shape::RankOp>(loc, rewriter.getIndexType(), rhs_shape);
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Value greater_rank_lhs =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::sgt, lhs_rank, rhs_rank);
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Value greater_rank =
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rewriter.create<SelectOp>(loc, greater_rank_lhs, lhs_rank, rhs_rank);
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// Generate a list of nested if/else statements to handle rank
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// specializations from 1 to `kMaxRankSpecialization`.
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scf::IfOp if_op = createIfOpForRankSpecializedBroadcastAndOp(
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rewriter, op, greater_rank, 1);
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OpBuilder if_builder = if_op.getThenBodyBuilder(rewriter.getListener());
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createRankSpecializedBroadcastAndOp(if_builder, op, lhs, rhs, 1);
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// Put each subsequent rank specialization inside the else statement of the
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// previous one.
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OpBuilder else_builder = if_op.getElseBodyBuilder(rewriter.getListener());
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constexpr int kMaxRankSpecialization = 6;
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for (int i = 2; i < kMaxRankSpecialization; i++) {
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auto inner_if = createIfOpForRankSpecializedBroadcastAndOp(
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else_builder, op, greater_rank, i);
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if_builder = inner_if.getThenBodyBuilder(rewriter.getListener());
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createRankSpecializedBroadcastAndOp(if_builder, op, lhs, rhs, i);
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else_builder.create<scf::YieldOp>(loc, inner_if.getResult(0));
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else_builder = inner_if.getElseBodyBuilder(rewriter.getListener());
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}
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// Fire an assertion if none of the rank specializations applied (one of
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// the ranks was greater than `kMaxRankSpecialization`).
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else_builder.create<AssertOp>(
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loc,
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GreaterRankIsN(else_builder, op.getLoc(), greater_rank,
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kMaxRankSpecialization),
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"Input for dynamic binary op lowering was of a rank greater than " +
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std::to_string(kMaxRankSpecialization));
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// Add the rank 6 specialization to the innermost else block.
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createRankSpecializedBroadcastAndOp(else_builder, op, lhs, rhs,
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kMaxRankSpecialization);
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// Return the result of the outermost if statement.
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return if_op.getResult(0);
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}
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};
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struct TransformUnrankedHloPass
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@ -209,9 +209,9 @@ func @addUnrankedUnranked(
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// CHECK-NEXT: %[[CONST_SHAPE_1:.*]] = shape.const_shape [1]
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// CHECK-NEXT: %[[BROADCASTED_LHS_1:.*]] = shape.broadcast %[[LHS_SHAPE]], %[[CONST_SHAPE_1]] : tensor<?xindex>, tensor<1xindex> -> tensor<?xindex>
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// CHECK-NEXT: %[[CASTED_LHS_1:.*]] = tensor.cast %[[BROADCASTED_LHS_1]] : tensor<?xindex> to tensor<1xindex>
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// CHECK-NEXT: %[[RESHAPED_LHS_1:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_1]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// CHECK-NEXT: %[[BROADCASTED_RHS_1:.*]] = shape.broadcast %[[RHS_SHAPE]], %[[CONST_SHAPE_1]] : tensor<?xindex>, tensor<1xindex> -> tensor<?xindex>
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// CHECK-NEXT: %[[CASTED_RHS_1:.*]] = tensor.cast %[[BROADCASTED_RHS_1]] : tensor<?xindex> to tensor<1xindex>
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// CHECK-NEXT: %[[RESHAPED_LHS_1:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_1]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// CHECK-NEXT: %[[RESHAPED_RHS_1:.*]] = "mhlo.dynamic_reshape"(%[[RHS]], %[[CASTED_RHS_1]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
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// CHECK-NEXT: %[[RESULT_RANK_1:.*]] = chlo.broadcast_add %[[RESHAPED_LHS_1]], %[[RESHAPED_RHS_1]] : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
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// CHECK-NEXT: %[[RESULT_1:.*]] = tensor.cast %[[RESULT_RANK_1]] : tensor<?xf32> to tensor<*xf32>
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@ -224,9 +224,9 @@ func @addUnrankedUnranked(
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// CHECK-NEXT: %[[CONST_SHAPE_2:.*]] = shape.const_shape [1, 1]
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// CHECK-NEXT: %[[BROADCASTED_LHS_2:.*]] = shape.broadcast %[[LHS_SHAPE]], %[[CONST_SHAPE_2]] : tensor<?xindex>, tensor<2xindex> -> tensor<?xindex>
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// CHECK-NEXT: %[[CASTED_LHS_2:.*]] = tensor.cast %[[BROADCASTED_LHS_2]] : tensor<?xindex> to tensor<2xindex>
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// CHECK-NEXT: %[[RESHAPED_LHS_2:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_2]]) : (tensor<*xf32>, tensor<2xindex>) -> tensor<?x?xf32>
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// CHECK-NEXT: %[[BROADCASTED_RHS_2:.*]] = shape.broadcast %[[RHS_SHAPE]], %[[CONST_SHAPE_2]] : tensor<?xindex>, tensor<2xindex> -> tensor<?xindex>
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// CHECK-NEXT: %[[CASTED_RHS_2:.*]] = tensor.cast %[[BROADCASTED_RHS_2]] : tensor<?xindex> to tensor<2xindex>
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// CHECK-NEXT: %[[RESHAPED_LHS_2:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_2]]) : (tensor<*xf32>, tensor<2xindex>) -> tensor<?x?xf32>
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// CHECK-NEXT: %[[RESHAPED_RHS_2:.*]] = "mhlo.dynamic_reshape"(%[[RHS]], %[[CASTED_RHS_2]]) : (tensor<*xf32>, tensor<2xindex>) -> tensor<?x?xf32>
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// CHECK-NEXT: %[[RESULT_RANK_2:.*]] = chlo.broadcast_add %[[RESHAPED_LHS_2]], %[[RESHAPED_RHS_2]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK-NEXT: %[[RESULT_2:.*]] = tensor.cast %[[RESULT_RANK_2]] : tensor<?x?xf32> to tensor<*xf32>
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@ -239,9 +239,9 @@ func @addUnrankedUnranked(
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// CHECK-NEXT: %[[CONST_SHAPE_3:.*]] = shape.const_shape [1, 1, 1]
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// CHECK-NEXT: %[[BROADCASTED_LHS_3:.*]] = shape.broadcast %[[LHS_SHAPE]], %[[CONST_SHAPE_3]] : tensor<?xindex>, tensor<3xindex> -> tensor<?xindex>
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// CHECK-NEXT: %[[CASTED_LHS_3:.*]] = tensor.cast %[[BROADCASTED_LHS_3]] : tensor<?xindex> to tensor<3xindex>
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// CHECK-NEXT: %[[RESHAPED_LHS_3:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_3]]) : (tensor<*xf32>, tensor<3xindex>) -> tensor<?x?x?xf32>
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// CHECK-NEXT: %[[BROADCASTED_RHS_3:.*]] = shape.broadcast %[[RHS_SHAPE]], %[[CONST_SHAPE_3]] : tensor<?xindex>, tensor<3xindex> -> tensor<?xindex>
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// CHECK-NEXT: %[[CASTED_RHS_3:.*]] = tensor.cast %[[BROADCASTED_RHS_3]] : tensor<?xindex> to tensor<3xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_3:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_3]]) : (tensor<*xf32>, tensor<3xindex>) -> tensor<?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESHAPED_RHS_3:.*]] = "mhlo.dynamic_reshape"(%[[RHS]], %[[CASTED_RHS_3]]) : (tensor<*xf32>, tensor<3xindex>) -> tensor<?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_RANK_3:.*]] = chlo.broadcast_add %[[RESHAPED_LHS_3]], %[[RESHAPED_RHS_3]] : (tensor<?x?x?xf32>, tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_3:.*]] = tensor.cast %[[RESULT_RANK_3]] : tensor<?x?x?xf32> to tensor<*xf32>
|
||||
|
|
@ -254,9 +254,9 @@ func @addUnrankedUnranked(
|
|||
// CHECK-NEXT: %[[CONST_SHAPE_4:.*]] = shape.const_shape [1, 1, 1, 1]
|
||||
// CHECK-NEXT: %[[BROADCASTED_LHS_4:.*]] = shape.broadcast %[[LHS_SHAPE]], %[[CONST_SHAPE_4]] : tensor<?xindex>, tensor<4xindex> -> tensor<?xindex>
|
||||
// CHECK-NEXT: %[[CASTED_LHS_4:.*]] = tensor.cast %[[BROADCASTED_LHS_4]] : tensor<?xindex> to tensor<4xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_4:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_4]]) : (tensor<*xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[BROADCASTED_RHS_4:.*]] = shape.broadcast %[[RHS_SHAPE]], %[[CONST_SHAPE_4]] : tensor<?xindex>, tensor<4xindex> -> tensor<?xindex>
|
||||
// CHECK-NEXT: %[[CASTED_RHS_4:.*]] = tensor.cast %[[BROADCASTED_RHS_4]] : tensor<?xindex> to tensor<4xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_4:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_4]]) : (tensor<*xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESHAPED_RHS_4:.*]] = "mhlo.dynamic_reshape"(%[[RHS]], %[[CASTED_RHS_4]]) : (tensor<*xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_RANK_4:.*]] = chlo.broadcast_add %[[RESHAPED_LHS_4]], %[[RESHAPED_RHS_4]] : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_4:.*]] = tensor.cast %[[RESULT_RANK_4]] : tensor<?x?x?x?xf32> to tensor<*xf32>
|
||||
|
|
@ -269,9 +269,9 @@ func @addUnrankedUnranked(
|
|||
// CHECK-NEXT: %[[CONST_SHAPE_5:.*]] = shape.const_shape [1, 1, 1, 1, 1]
|
||||
// CHECK-NEXT: %[[BROADCASTED_LHS_5:.*]] = shape.broadcast %[[LHS_SHAPE]], %[[CONST_SHAPE_5]] : tensor<?xindex>, tensor<5xindex> -> tensor<?xindex>
|
||||
// CHECK-NEXT: %[[CASTED_LHS_5:.*]] = tensor.cast %[[BROADCASTED_LHS_5]] : tensor<?xindex> to tensor<5xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_5:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_5]]) : (tensor<*xf32>, tensor<5xindex>) -> tensor<?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[BROADCASTED_RHS_5:.*]] = shape.broadcast %[[RHS_SHAPE]], %[[CONST_SHAPE_5]] : tensor<?xindex>, tensor<5xindex> -> tensor<?xindex>
|
||||
// CHECK-NEXT: %[[CASTED_RHS_5:.*]] = tensor.cast %[[BROADCASTED_RHS_5]] : tensor<?xindex> to tensor<5xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_5:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_5]]) : (tensor<*xf32>, tensor<5xindex>) -> tensor<?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESHAPED_RHS_5:.*]] = "mhlo.dynamic_reshape"(%[[RHS]], %[[CASTED_RHS_5]]) : (tensor<*xf32>, tensor<5xindex>) -> tensor<?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_RANK_5:.*]] = chlo.broadcast_add %[[RESHAPED_LHS_5]], %[[RESHAPED_RHS_5]] : (tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_5:.*]] = tensor.cast %[[RESULT_RANK_5]] : tensor<?x?x?x?x?xf32> to tensor<*xf32>
|
||||
|
|
@ -284,9 +284,9 @@ func @addUnrankedUnranked(
|
|||
// CHECK-NEXT: %[[CONST_SHAPE_6:.*]] = shape.const_shape [1, 1, 1, 1, 1, 1]
|
||||
// CHECK-NEXT: %[[BROADCASTED_LHS_6:.*]] = shape.broadcast %[[LHS_SHAPE]], %[[CONST_SHAPE_6]] : tensor<?xindex>, tensor<6xindex> -> tensor<?xindex>
|
||||
// CHECK-NEXT: %[[CASTED_LHS_6:.*]] = tensor.cast %[[BROADCASTED_LHS_6]] : tensor<?xindex> to tensor<6xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_6:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_6]]) : (tensor<*xf32>, tensor<6xindex>) -> tensor<?x?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[BROADCASTED_RHS_6:.*]] = shape.broadcast %[[RHS_SHAPE]], %[[CONST_SHAPE_6]] : tensor<?xindex>, tensor<6xindex> -> tensor<?xindex>
|
||||
// CHECK-NEXT: %[[CASTED_RHS_6:.*]] = tensor.cast %[[BROADCASTED_RHS_6]] : tensor<?xindex> to tensor<6xindex>
|
||||
// CHECK-NEXT: %[[RESHAPED_LHS_6:.*]] = "mhlo.dynamic_reshape"(%[[LHS]], %[[CASTED_LHS_6]]) : (tensor<*xf32>, tensor<6xindex>) -> tensor<?x?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESHAPED_RHS_6:.*]] = "mhlo.dynamic_reshape"(%[[RHS]], %[[CASTED_RHS_6]]) : (tensor<*xf32>, tensor<6xindex>) -> tensor<?x?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_RANK_6:.*]] = chlo.broadcast_add %[[RESHAPED_LHS_6]], %[[RESHAPED_RHS_6]] : (tensor<?x?x?x?x?x?xf32>, tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32>
|
||||
// CHECK-NEXT: %[[RESULT_6:.*]] = tensor.cast %[[RESULT_RANK_6]] : tensor<?x?x?x?x?x?xf32> to tensor<*xf32>
|
||||
|
|
|
|||
Loading…
Reference in New Issue