Add support for lowering mhlo.dynamic-update-slice ops to Linalg and std ops.
PiperOrigin-RevId: 376042810
This commit is contained in:
Hanhan Wang
TensorFlow MLIR Team
parent
1c7415ba0b
commit
28c411606f
|
|
@ -1316,6 +1316,72 @@ class DynamicSliceConverter : public OpConversionPattern<mhlo::DynamicSliceOp> {
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|
||||||
|
class DynamicUpdateSliceConverter
|
||||||
|
: public OpConversionPattern<mhlo::DynamicUpdateSliceOp> {
|
||||||
|
public:
|
||||||
|
using OpConversionPattern<mhlo::DynamicUpdateSliceOp>::OpConversionPattern;
|
||||||
|
|
||||||
|
LogicalResult matchAndRewrite(
|
||||||
|
mhlo::DynamicUpdateSliceOp op, ArrayRef<Value> args,
|
||||||
|
ConversionPatternRewriter& rewriter) const final {
|
||||||
|
auto loc = op.getLoc();
|
||||||
|
mhlo::DynamicUpdateSliceOp::Adaptor adaptor(args);
|
||||||
|
auto operand_type =
|
||||||
|
adaptor.operand().getType().dyn_cast<RankedTensorType>();
|
||||||
|
if (!operand_type || !operand_type.hasStaticShape()) {
|
||||||
|
return rewriter.notifyMatchFailure(
|
||||||
|
op, "require static ranked type for operand");
|
||||||
|
}
|
||||||
|
|
||||||
|
auto update_type = adaptor.update().getType().dyn_cast<RankedTensorType>();
|
||||||
|
if (!update_type || !update_type.hasStaticShape()) {
|
||||||
|
return rewriter.notifyMatchFailure(
|
||||||
|
op, "require static ranked type for operand");
|
||||||
|
}
|
||||||
|
|
||||||
|
// We do not have to clamp sizes because the semantic of `update`
|
||||||
|
// guarantees that it is always in the bounds. See
|
||||||
|
// https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice
|
||||||
|
SmallVector<OpFoldResult, 3> sizes;
|
||||||
|
for (auto size : update_type.getShape()) {
|
||||||
|
sizes.push_back(rewriter.getIndexAttr(size));
|
||||||
|
}
|
||||||
|
|
||||||
|
auto index_type = rewriter.getIndexType();
|
||||||
|
SmallVector<OpFoldResult, 3> start_indices;
|
||||||
|
Value zero = rewriter.create<ConstantOp>(
|
||||||
|
loc, rewriter.getZeroAttr(operand_type.getElementType()));
|
||||||
|
for (auto en : llvm::enumerate(adaptor.start_indices())) {
|
||||||
|
// By mhlo.DynamicUpdateSlice definition:
|
||||||
|
// `start_indices[i] = clamp(start_indices[i],
|
||||||
|
// 0, operand.dimension_size[i] - update.dimension_size[i])`
|
||||||
|
Value start_index = rewriter.create<tensor::ExtractOp>(loc, en.value());
|
||||||
|
Value ub = rewriter.create<ConstantOp>(
|
||||||
|
loc, rewriter.getIntegerAttr(operand_type.getElementType(),
|
||||||
|
operand_type.getDimSize(en.index()) -
|
||||||
|
update_type.getDimSize(en.index())));
|
||||||
|
// TODO(hanchung): This is a workaround to use the method because only
|
||||||
|
// lmhlo version is defined. The implementation in
|
||||||
|
// map_lmhlo_to_scalar_op.h requires to pass a mhlo op. It will convert it
|
||||||
|
// to an lmhlo op and call the lmhlo implementation.
|
||||||
|
start_index = lmhlo::HloOpToStdScalarOp::map<lmhlo::ClampOp>(
|
||||||
|
loc, start_index.getType(),
|
||||||
|
ArrayRef<Type>{start_index.getType(), start_index.getType(),
|
||||||
|
start_index.getType()},
|
||||||
|
ArrayRef<Value>{zero, start_index, ub}, &rewriter);
|
||||||
|
start_indices.push_back(
|
||||||
|
rewriter.create<IndexCastOp>(loc, index_type, start_index)
|
||||||
|
.getResult());
|
||||||
|
}
|
||||||
|
|
||||||
|
int64_t rank = operand_type.getRank();
|
||||||
|
SmallVector<OpFoldResult, 3> strides(rank, rewriter.getI64IntegerAttr(1));
|
||||||
|
rewriter.replaceOpWithNewOp<SubTensorInsertOp>(
|
||||||
|
op, adaptor.update(), adaptor.operand(), start_indices, sizes, strides);
|
||||||
|
return success();
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
enum class DotOperationType {
|
enum class DotOperationType {
|
||||||
kVectorDot = 0,
|
kVectorDot = 0,
|
||||||
kMatrixVector = 1,
|
kMatrixVector = 1,
|
||||||
|
|
@ -2440,6 +2506,7 @@ void populateHLOToLinalgConversionPattern(MLIRContext* context,
|
||||||
ReverseConverter<mhlo::ReverseOp, false>,
|
ReverseConverter<mhlo::ReverseOp, false>,
|
||||||
SliceConverter<mhlo::SliceOp, false>,
|
SliceConverter<mhlo::SliceOp, false>,
|
||||||
DynamicSliceConverter,
|
DynamicSliceConverter,
|
||||||
|
DynamicUpdateSliceConverter,
|
||||||
TransposeConverter<mhlo::TransposeOp, false>,
|
TransposeConverter<mhlo::TransposeOp, false>,
|
||||||
DotOpOnTensorsConversion<DotOperationType::kMatrixMatrix,
|
DotOpOnTensorsConversion<DotOperationType::kMatrixMatrix,
|
||||||
linalg::MatmulOp>,
|
linalg::MatmulOp>,
|
||||||
|
|
|
||||||
|
|
@ -1708,6 +1708,73 @@ func @dynamic_slice_unsigned(%arg: tensor<3x4xui32>, %start1: tensor<i64>, %star
|
||||||
// CHECK: %[[START2:.*]] = index_cast %[[CLAMPED2]] : i64 to index
|
// CHECK: %[[START2:.*]] = index_cast %[[CLAMPED2]] : i64 to index
|
||||||
// CHECK: subtensor %[[SIGNLESS_ARG0]][%[[START1]], %[[START2]]] [1, 4] [1, 1]
|
// CHECK: subtensor %[[SIGNLESS_ARG0]][%[[START1]], %[[START2]]] [1, 4] [1, 1]
|
||||||
|
|
||||||
|
// -----
|
||||||
|
|
||||||
|
func @dynamic_update_slice(%target: tensor<3x3xi32>, %update: tensor<2x2xi32>, %c0: tensor<i32>) -> tensor<3x3xi32> {
|
||||||
|
%0 = "mhlo.dynamic-update-slice"(%target, %update, %c0, %c0)
|
||||||
|
: (tensor<3x3xi32>, tensor<2x2xi32>, tensor<i32>, tensor<i32>) -> tensor<3x3xi32>
|
||||||
|
return %0 : tensor<3x3xi32>
|
||||||
|
}
|
||||||
|
// CHECK-LABEL: func @dynamic_update_slice(
|
||||||
|
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]
|
||||||
|
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]
|
||||||
|
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]
|
||||||
|
// CHECK: %[[C0:.*]] = constant 0 : i32
|
||||||
|
// CHECK: %[[SCALAR1:.*]] = tensor.extract %[[ARG2]][] : tensor<i32>
|
||||||
|
// CHECK: %[[UB1:.*]] = constant 1 : i32
|
||||||
|
// CHECK: %[[COND1:.*]] = cmpi slt, %[[SCALAR1]], %[[UB1]] : i32
|
||||||
|
// CHECK: %[[T1:.*]] = select %[[COND1]], %[[SCALAR1]], %[[UB1]] : i32
|
||||||
|
// CHECK: %[[COND2:.*]] = cmpi sgt, %[[T1]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[CLAMPED1:.*]] = select %[[COND2]], %[[T1]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[START1:.*]] = index_cast %[[CLAMPED1]] : i32 to index
|
||||||
|
// CHECK: %[[SCALAR2:.*]] = tensor.extract %[[ARG2]][] : tensor<i32>
|
||||||
|
// CHECK: %[[UB2:.*]] = constant 1 : i32
|
||||||
|
// CHECK: %[[COND3:.*]] = cmpi slt, %[[SCALAR2]], %[[UB2]] : i32
|
||||||
|
// CHECK: %[[T2:.*]] = select %[[COND3]], %[[SCALAR2]], %[[UB2]] : i32
|
||||||
|
// CHECK: %[[COND4:.*]] = cmpi sgt, %[[T2]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[CLAMPED2:.*]] = select %[[COND4]], %[[T2]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[START2:.*]] = index_cast %[[CLAMPED2]] : i32 to index
|
||||||
|
// CHECK: %[[RES:.*]] = subtensor_insert %[[ARG1]] into %[[ARG0]]
|
||||||
|
// CHECK-SAME: [%[[START1]], %[[START2]]] [2, 2] [1, 1]
|
||||||
|
// CHECK-SAME: : tensor<2x2xi32> into tensor<3x3xi32>
|
||||||
|
// CHECK: return %[[RES]] : tensor<3x3xi32>
|
||||||
|
|
||||||
|
// -----
|
||||||
|
|
||||||
|
func @dynamic_update_slice_unsigned(%target: tensor<3x3xui32>, %update: tensor<2x2xui32>, %c0: tensor<i32>) -> tensor<3x3xui32> {
|
||||||
|
%0 = "mhlo.dynamic-update-slice"(%target, %update, %c0, %c0)
|
||||||
|
: (tensor<3x3xui32>, tensor<2x2xui32>, tensor<i32>, tensor<i32>) -> tensor<3x3xui32>
|
||||||
|
return %0 : tensor<3x3xui32>
|
||||||
|
}
|
||||||
|
// CHECK-LABEL: func @dynamic_update_slice_unsigned(
|
||||||
|
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]
|
||||||
|
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]
|
||||||
|
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]
|
||||||
|
// CHECK: %[[SIGNLESS_TARGET:.*]] = unrealized_conversion_cast %[[ARG0]] : tensor<3x3xui32> to tensor<3x3xi32>
|
||||||
|
// CHECK: %[[SIGNLESS_UPDATE:.*]] = unrealized_conversion_cast %[[ARG1]] : tensor<2x2xui32> to tensor<2x2xi32>
|
||||||
|
// CHECK: %[[C0:.*]] = constant 0 : i32
|
||||||
|
// CHECK: %[[SCALAR1:.*]] = tensor.extract %[[ARG2]][] : tensor<i32>
|
||||||
|
// CHECK: %[[UB1:.*]] = constant 1 : i32
|
||||||
|
// CHECK: %[[COND1:.*]] = cmpi slt, %[[SCALAR1]], %[[UB1]] : i32
|
||||||
|
// CHECK: %[[T1:.*]] = select %[[COND1]], %[[SCALAR1]], %[[UB1]] : i32
|
||||||
|
// CHECK: %[[COND2:.*]] = cmpi sgt, %[[T1]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[CLAMPED1:.*]] = select %[[COND2]], %[[T1]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[START1:.*]] = index_cast %[[CLAMPED1]] : i32 to index
|
||||||
|
// CHECK: %[[SCALAR2:.*]] = tensor.extract %[[ARG2]][] : tensor<i32>
|
||||||
|
// CHECK: %[[UB2:.*]] = constant 1 : i32
|
||||||
|
// CHECK: %[[COND3:.*]] = cmpi slt, %[[SCALAR2]], %[[UB2]] : i32
|
||||||
|
// CHECK: %[[T2:.*]] = select %[[COND3]], %[[SCALAR2]], %[[UB2]] : i32
|
||||||
|
// CHECK: %[[COND4:.*]] = cmpi sgt, %[[T2]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[CLAMPED2:.*]] = select %[[COND4]], %[[T2]], %[[C0]] : i32
|
||||||
|
// CHECK: %[[START2:.*]] = index_cast %[[CLAMPED2]] : i32 to index
|
||||||
|
// CHECK: %[[SIGNLESS_RES:.*]] = subtensor_insert %[[SIGNLESS_UPDATE]] into %[[SIGNLESS_TARGET]]
|
||||||
|
// CHECK-SAME: [%[[START1]], %[[START2]]] [2, 2] [1, 1]
|
||||||
|
// CHECK-SAME: : tensor<2x2xi32> into tensor<3x3xi32>
|
||||||
|
// CHECK: %[[RES:.*]] = unrealized_conversion_cast %[[SIGNLESS_RES]] : tensor<3x3xi32> to tensor<3x3xui32>
|
||||||
|
// CHECK: return %[[RES]] : tensor<3x3xui32>
|
||||||
|
|
||||||
|
// -----
|
||||||
|
|
||||||
func @pad_cst(%arg0: tensor<12x4xf32>) -> tensor<18x12xf32> {
|
func @pad_cst(%arg0: tensor<12x4xf32>) -> tensor<18x12xf32> {
|
||||||
%0 = constant dense<0.0> : tensor<f32>
|
%0 = constant dense<0.0> : tensor<f32>
|
||||||
%1 = "mhlo.pad"(%arg0, %0) {
|
%1 = "mhlo.pad"(%arg0, %0) {
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue