colin
/
mlir-hlo
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Cleanup build rule names in compiler/mlir/hlo to remove the redundant/obsolete xla_ prefix 

PiperOrigin-RevId: 320320140
This commit is contained in:
Mehdi Amini 2020-07-09 03:32:16 +00:00 committed by Mehdi Amini
parent f4303855c4
commit 506ddd9c4a
51 changed files with 222 additions and 228 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
include/mlir-hlo/Dialect/mhlo/IR/chlo_ops.td
View File

@ -38,7 +38,7 @@ def HLOClient_Dialect : Dialect {
let name = "chlo";
let cppNamespace = "chlo";
let summary = [{
XLA Client HLO Ops
Client HLO Ops
}];
let description = [{
@ -60,7 +60,7 @@ class HLOClient_Op<string mnemonic, list<OpTrait> traits> :
}
//===----------------------------------------------------------------------===//
// XLA binary elementwise op definitions.
// CHLO binary elementwise op definitions.
// From the client perspective, each of these support both explicit rank
// broadcasting (via the broadcast_dimensions attribute) and implicit degenerate
// shape broadcasting.

2
include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.h
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file defines the operations used in the XLA dialect.
// This file defines the operations used in the MHLO dialect.
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_IR_HLO_OPS_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_IR_HLO_OPS_H_

29
include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td
View File

@ -13,10 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This is the operation definition file for XLA HLO ops which map to the
// traditional definition in xla_data.proto (or are aligned with the goals
// thereof).
// See: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/xla_data.proto
// This is the operation definition file for MHLO ops.
#ifndef HLO_OPS
#define HLO_OPS
@ -44,7 +41,7 @@ class HLO_Op<string mnemonic, list<OpTrait> traits> :
}
//===----------------------------------------------------------------------===//
// XLA nullary op definitions.
// MHLO nullary op definitions.
//===----------------------------------------------------------------------===//
def HLO_ConstOp : HLO_Op<"constant",
@ -113,7 +110,7 @@ def HLO_CreateTokenOp : HLO_Op<"create_token", [NoSideEffect]> {
}
//===----------------------------------------------------------------------===//
// XLA unary elementwise op definitions.
// MHLO unary elementwise op definitions.
//===----------------------------------------------------------------------===//
// See https://www.tensorflow.org/xla/operation_semantics#element-wise_unary_functions
@ -264,7 +261,7 @@ def HLO_TanhOp: HLO_UnaryElementwiseOp<"tanh",
HLO_FpOrComplexTensor>, BASE_HLO_TanhOp;
//===----------------------------------------------------------------------===//
// XLA binary elementwise op definitions.
// MHLO binary elementwise op definitions.
//===----------------------------------------------------------------------===//
// See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations
@ -363,7 +360,7 @@ def HLO_SubOp : HLO_BinaryElementwiseOp<"subtract",
}
//===----------------------------------------------------------------------===//
// XLA binary logical elementwise op definitions.
// MHLO binary logical elementwise op definitions.
//===----------------------------------------------------------------------===//
// See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations
@ -381,7 +378,7 @@ def HLO_OrOp: HLO_BinaryLogicalElementwiseOp<"or">, BASE_HLO_OrOp;
def HLO_XorOp : HLO_BinaryLogicalElementwiseOp<"xor">, BASE_HLO_XorOp;
//===----------------------------------------------------------------------===//
// XLA communication op definitions.
// MHLO communication op definitions.
//===----------------------------------------------------------------------===//
// InfeedOp corresponds to 'InfeedWithToken' xla client API and not 'Infeed'.
@ -481,7 +478,7 @@ def HLO_RecvOp : HLO_Op<"recv", []> {
}
//===----------------------------------------------------------------------===//
// XLA parallelism related op definitions.
// MHLO parallelism related op definitions.
//===----------------------------------------------------------------------===//
def HLO_ReplicaIdOp : HLO_Op<"replica_id", [NoSideEffect]>,
@ -492,7 +489,7 @@ def HLO_ReplicaIdOp : HLO_Op<"replica_id", [NoSideEffect]>,
}
//===----------------------------------------------------------------------===//
// XLA control flow op definitions.
// MHLO control flow op definitions.
//===----------------------------------------------------------------------===//
def HLO_AfterAllOp : HLO_Op<"after_all", [NoSideEffect]> {
@ -640,7 +637,7 @@ def HLO_ReduceOp: HLO_Op<"reduce", [
}
//===----------------------------------------------------------------------===//
// XLA tuple op definitions.
// MHLO tuple op definitions.
//===----------------------------------------------------------------------===//
def HLO_GetTupleElementOp: HLO_Op<"get_tuple_element", [NoSideEffect]>, BASE_HLO_GetTupleElementOp {
let arguments = (ins
@ -684,7 +681,7 @@ def HLO_CompareOp: HLO_Op<"compare",
}
//===----------------------------------------------------------------------===//
// XLA Slice definitions.
// MHLO Slice definitions.
//===----------------------------------------------------------------------===//
def HLO_SliceOp: HLO_Op<
@ -745,7 +742,7 @@ def HLO_DynamicUpdateSliceOp: HLO_Op<"dynamic-update-slice",
//===----------------------------------------------------------------------===//
// XLA Other op definitions.
// MHLO Other op definitions.
//===----------------------------------------------------------------------===//
def HLO_BatchNormGradOp : HLO_Op<"batch_norm_grad", [NoSideEffect]>,
@ -1320,7 +1317,7 @@ def HLO_TorchIndexSelectOp : HLO_Op<"torch_index_select", [NoSideEffect]> {
}
//===----------------------------------------------------------------------===//
// XLA RngUniform Operator.
// MHLO RngUniform Operator.
//===----------------------------------------------------------------------===//
def HLO_RngUniformOp : HLO_Op<"rng_uniform", []>, BASE_HLO_RngUniformOp {
let arguments = (ins
@ -1347,7 +1344,7 @@ def HLO_RngNormalOp : HLO_Op<"rng_normal", []>, BASE_HLO_RngNormalOp {
}
//===----------------------------------------------------------------------===//
// XLA Quantize Operator.
// MHLO Quantize Operator.
//===----------------------------------------------------------------------===//
def HLO_DequantizeOp : HLO_Op<"dequantize", [NoSideEffect]>,
BASE_HLO_DequantizeOp {

8
include/mlir-hlo/Dialect/mhlo/IR/hlo_ops_base.td
View File

@ -35,7 +35,7 @@ def HLO_Complex : Complex<AnyTypeOf<[F32, F64]>>;
defvar BroadcastDimAttr = I64ElementsAttr;
//===----------------------------------------------------------------------===//
// XLA on tensors type definitions.
// MHLO on tensors type definitions.
//===----------------------------------------------------------------------===//
// Token type.
@ -78,7 +78,7 @@ def HLO_StaticShapeTensor : StaticShapeTensorOf<[
AnyFloat, HLO_Pred, HLO_Int, HLO_Complex]>;
//===----------------------------------------------------------------------===//
// XLA on tensors combined type definitions.
// MHLO on tensors combined type definitions.
//===----------------------------------------------------------------------===//
// Any integer or floating-point tensor types
@ -97,7 +97,7 @@ def HLO_IntFpOrComplexTensor : TensorOf<[HLO_Int, AnyFloat, HLO_Complex]>;
def HLO_PredIntOrFpTensor : TensorOf<[HLO_Pred, HLO_Int, AnyFloat]>;
//===----------------------------------------------------------------------===//
// XLA nullary op definitions.
// MHLO nullary op definitions.
//===----------------------------------------------------------------------===//
class BASE_HLO_ConstOp {
@ -117,7 +117,7 @@ class BASE_HLO_IotaOp {
}
//===----------------------------------------------------------------------===//
// XLA unary elementwise op definitions.
// MHLO unary elementwise op definitions.
//===----------------------------------------------------------------------===//
// See https://www.tensorflow.org/xla/operation_semantics#element-wise_unary_functions

8
include/mlir-hlo/Dialect/mhlo/IR/hlo_utils.td
View File

@ -25,19 +25,19 @@ def NullArrayAttr : NativeCodeCall<"ArrayAttr()">;
def CastIntElementsAttr : NativeCodeCall<"$0.cast<DenseIntElementsAttr>()">;
class ConstantSplat<string value> : NativeCodeCall<
"xla::getSplat(&$_builder, $0, " # value # ")">;
"hlo::getSplat(&$_builder, $0, " # value # ")">;
def NullDenseIntElementsAttr : NativeCodeCall<"DenseIntElementsAttr()">;
def BinBroadcastDimensions : NativeCodeCall<
"xla::getBroadcastDimensionsAttr(&$_builder, $0, $1)">;
"hlo::getBroadcastDimensionsAttr(&$_builder, $0, $1)">;
def BinBroadcastDimensionsNonEmpty : NativeCodeCall<
"xla::getBroadcastDimensionsAttr(&$_builder, $0, $1, /*allow_empty=*/false)">;
"hlo::getBroadcastDimensionsAttr(&$_builder, $0, $1, /*allow_empty=*/false)">;
// Here, the element type can be any integer or float type. But, note that only
// 32 bit integers are supported for the value.
class GetScalarOfType<int value> : NativeCodeCall<
"xla::GetScalarOfType(getElementTypeOrSelf($0)," # value # ")">;
"hlo::GetScalarOfType(getElementTypeOrSelf($0)," # value # ")">;
#endif // HLO_UTILS

21
include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.td
View File

@ -13,9 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This is the operation definition file for LXLA.
// This is the operation definition file for LMHLO, the "late" MHLO variant of
// the dialect, which operates on buffers instead of tensors.
//
// This file largely overlaps with hlo_ops.td at a logic level. It's tempting to
// This file largely overlaps with mhlo_ops.td at a logic level. It's tempting to
// merge these two files together, but we need to consider the following
// obstacles:
// * We need to have a common representation for arguments. That is to say,
@ -43,7 +44,7 @@ def LHLO_Dialect : Dialect {
}
//===----------------------------------------------------------------------===//
// XLA type definitions.
// LMHLO type definitions.
//===----------------------------------------------------------------------===//
// Any integer tensor types
@ -66,7 +67,7 @@ def LHLO_PredOrIntBuffer : MemRefOf<[HLO_Int, HLO_Pred]>;
def LHLO_Buffer : MemRefOf<[AnyFloat, AnySignlessInteger, AnyComplex]>;
//===----------------------------------------------------------------------===//
// XLA nullary op definitions.
// LMHLO nullary op definitions.
//===----------------------------------------------------------------------===//
class LHLO_Op<string mnemonic, list<OpTrait> traits> :
@ -86,7 +87,7 @@ def LHLO_IotaOp : LHLO_Op<"iota", []>, BASE_HLO_IotaOp {
}
//===----------------------------------------------------------------------===//
// XLA unary elementwise op definitions.
// LMHLO unary elementwise op definitions.
//===----------------------------------------------------------------------===//
// See https://www.tensorflow.org/xla/operation_semantics#element-wise_unary_functions
@ -157,7 +158,7 @@ def LHLO_SinOp: LHLO_UnaryElementwiseOp<"sine", LHLO_FpOrComplexBuffer>, BASE_HL
def LHLO_TanhOp: LHLO_UnaryElementwiseOp<"tanh", LHLO_FpOrComplexBuffer>, BASE_HLO_TanhOp;
//===----------------------------------------------------------------------===//
// XLA binary elementwise op definitions.
// LMHLO binary elementwise op definitions.
//===----------------------------------------------------------------------===//
// See https://www.tensorflow.org/xla/operation_semantics#element-wise_binary_arithmetic_operations
@ -212,7 +213,7 @@ def LHLO_SubOp : LHLO_BinaryElementwiseOp<"subtract">, BASE_HLO_SubOp;
def LHLO_XorOp : LHLO_BinaryElementwiseOp<"xor", LHLO_PredOrIntBuffer>, BASE_HLO_XorOp;
//===----------------------------------------------------------------------===//
// XLA control flow op definitions.
// LMHLO control flow op definitions.
//===----------------------------------------------------------------------===//
// TODO(b/139813999): specify required function signature in a type-safe way.
@ -284,7 +285,7 @@ def LHLO_WhileOp: LHLO_Op<"while", [SameVariadicOperandSize]>,
}
//===----------------------------------------------------------------------===//
// XLA tuple op definitions.
// LMHLO tuple op definitions.
//===----------------------------------------------------------------------===//
def LHLO_CompareOp: LHLO_Op<"compare", []>, BASE_HLO_CompareOp {
@ -298,7 +299,7 @@ def LHLO_CompareOp: LHLO_Op<"compare", []>, BASE_HLO_CompareOp {
}
//===----------------------------------------------------------------------===//
// XLA Slice definitions.
// LMHLO Slice definitions.
//===----------------------------------------------------------------------===//
def LHLO_SliceOp: LHLO_Op<
@ -483,7 +484,7 @@ def ReshapeMemRefCastOp: Op<LHLO_Dialect, "reshape_memref_cast", [
//===----------------------------------------------------------------------===//
// XLA Other op definitions.
// LMHLO Other op definitions.
//===----------------------------------------------------------------------===//
def LHLO_BatchNormGradOp : LHLO_Op<"batch_norm_grad", []>,

53
include/mlir-hlo/Dialect/mhlo/transforms/map_xla_to_scalar_op.h → include/mlir-hlo/Dialect/mhlo/transforms/map_lmhlo_to_scalar_op.h
View File

@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_MAP_XLA_TO_SCALAR_OP_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_MAP_XLA_TO_SCALAR_OP_H_
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_MAP_LMHLO_TO_SCALAR_OP_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_MAP_LMHLO_TO_SCALAR_OP_H_
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/StringRef.h"
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/StringSwitch.h"
@ -150,15 +150,14 @@ inline Value MapLhloOpToStdScalarOp<lmhlo::AndOp>(Location loc,
}
template <typename PredicateType>
inline Optional<PredicateType> getCmpPredicate(
StringRef xla_comparison_direction) {
inline Optional<PredicateType> getCmpPredicate(StringRef comparison_direction) {
return llvm::None;
}
template <>
inline Optional<CmpFPredicate> getCmpPredicate<CmpFPredicate>(
StringRef xla_comparison_direction) {
return llvm::StringSwitch<Optional<CmpFPredicate>>(xla_comparison_direction)
StringRef comparison_direction) {
return llvm::StringSwitch<Optional<CmpFPredicate>>(comparison_direction)
.Case("EQ", CmpFPredicate::OEQ)
.Case("NE", CmpFPredicate::ONE)
.Case("GE", CmpFPredicate::OGE)
@ -170,8 +169,8 @@ inline Optional<CmpFPredicate> getCmpPredicate<CmpFPredicate>(
template <>
inline Optional<CmpIPredicate> getCmpPredicate<CmpIPredicate>(
StringRef xla_comparison_direction) {
return llvm::StringSwitch<Optional<CmpIPredicate>>(xla_comparison_direction)
StringRef comparison_direction) {
return llvm::StringSwitch<Optional<CmpIPredicate>>(comparison_direction)
.Case("EQ", CmpIPredicate::eq)
.Case("NE", CmpIPredicate::ne)
.Case("GE", CmpIPredicate::sge)
@ -181,8 +180,8 @@ inline Optional<CmpIPredicate> getCmpPredicate<CmpIPredicate>(
.Default(llvm::None);
}
template <typename XLACompareOpTy>
inline Value MapXlaCompareOpToStdScalarOp(Location loc,
template <typename CompareOpTy>
inline Value MapCompareOpToStdScalarOp(Location loc,
StringRef comparison_direction,
ArrayRef<Type> result_types,
ArrayRef<Value> args, OpBuilder* b) {
@ -193,14 +192,14 @@ inline Value MapXlaCompareOpToStdScalarOp(Location loc,
Optional<CmpIPredicate> predicate =
getCmpPredicate<CmpIPredicate>(comparison_direction);
assert(predicate.hasValue() && "expected valid comparison direction");
return b->create<ScalarIOp<XLACompareOpTy>>(loc, predicate.getValue(), lhs,
return b->create<ScalarIOp<CompareOpTy>>(loc, predicate.getValue(), lhs,
rhs);
}
if (element_type.isa<FloatType>()) {
Optional<CmpFPredicate> predicate =
getCmpPredicate<CmpFPredicate>(comparison_direction);
assert(predicate.hasValue() && "expected valid comparison direction");
return b->create<ScalarFOp<XLACompareOpTy>>(loc, predicate.getValue(), lhs,
return b->create<ScalarFOp<CompareOpTy>>(loc, predicate.getValue(), lhs,
rhs);
}
return nullptr;
@ -337,10 +336,10 @@ inline Value MapLhloOpToStdScalarOp<lmhlo::SinOp>(Location loc,
loc, result_types, args, b);
}
/// Implements the conversion of XLA op to scalar op (to use within region of a
/// Implements the conversion of HLO op to scalar op (to use within region of a
/// linalg.generic op) for compare-select style operations like min/max.
template <typename... Args>
struct XlaCompareSelectOpToStdScalarOp {
struct CompareSelectOpToStdScalarOp {
static Value map(Location loc, StringRef comparison_direction,
ArrayRef<Type> result_types, ArrayRef<Value> args,
OpBuilder* b) {
@ -352,7 +351,7 @@ struct XlaCompareSelectOpToStdScalarOp {
/// dialect with a given predicate based on the element type of the operand.
template <typename SupportedType, typename StdCompareOp, typename Predicate,
typename... Args>
struct XlaCompareSelectOpToStdScalarOp<SupportedType, StdCompareOp, Predicate,
struct CompareSelectOpToStdScalarOp<SupportedType, StdCompareOp, Predicate,
Args...> {
static Value map(Location loc, StringRef comparison_direction,
ArrayRef<Type> result_types, ArrayRef<Value> args,
@ -365,8 +364,8 @@ struct XlaCompareSelectOpToStdScalarOp<SupportedType, StdCompareOp, Predicate,
args[0], args[1]);
return b->create<::mlir::SelectOp>(loc, cmp, args[0], args[1]);
}
return XlaCompareSelectOpToStdScalarOp<Args...>::map(
loc, comparison_direction, result_types, args, b);
return CompareSelectOpToStdScalarOp<Args...>::map(loc, comparison_direction,
result_types, args, b);
}
};
@ -384,7 +383,7 @@ inline Value MapLhloOpToStdScalarOp<lmhlo::MaxOp>(Location loc,
ArrayRef<Type> result_types,
ArrayRef<Value> args,
OpBuilder* b) {
return XlaCompareSelectOpToStdScalarOp<
return CompareSelectOpToStdScalarOp<
IntegerType, ScalarIOp<lmhlo::CompareOp>, CmpIPredicate, FloatType,
ScalarFOp<lmhlo::CompareOp>, CmpFPredicate>::map(loc, "GT", result_types,
args, b);
@ -395,7 +394,7 @@ inline Value MapLhloOpToStdScalarOp<lmhlo::MinOp>(Location loc,
ArrayRef<Type> result_types,
ArrayRef<Value> args,
OpBuilder* b) {
return XlaCompareSelectOpToStdScalarOp<
return CompareSelectOpToStdScalarOp<
IntegerType, ScalarIOp<lmhlo::CompareOp>, CmpIPredicate, FloatType,
ScalarFOp<lmhlo::CompareOp>, CmpFPredicate>::map(loc, "LT", result_types,
args, b);
@ -475,25 +474,25 @@ inline Value MapLhloOpToStdScalarOp<lmhlo::TanhOp>(Location loc,
} // namespace impl
struct XlaOpToStdScalarOp {
struct HloOpToStdScalarOp {
// Implementation for LHLO ops except lmhlo::CompareOp.
template <typename XlaOpTy, typename LhloOpTy = XlaOpTy,
template <typename HloOpTy, typename LhloOpTy = HloOpTy,
typename = std::enable_if_t<
!std::is_same<LhloOpTy, lmhlo::CompareOp>::value &&
std::is_same<typename mhlo::HloToLhloOp<LhloOpTy>,
std::false_type>::value>>
static Value map(XlaOpTy op, ArrayRef<Type> result_types,
static Value map(HloOpTy op, ArrayRef<Type> result_types,
ArrayRef<Value> args, OpBuilder* b, unsigned i = 0) {
return impl::MapLhloOpToStdScalarOp<LhloOpTy>(op.getLoc(), result_types,
args, b);
}
// Implementation for HLO ops except mhlo::CompareOp.
template <typename XlaOpTy, typename LhloOpTy = mhlo::HloToLhloOp<XlaOpTy>,
template <typename HloOpTy, typename LhloOpTy = mhlo::HloToLhloOp<HloOpTy>,
typename = std::enable_if_t<
!std::is_same<LhloOpTy, lmhlo::CompareOp>::value &&
!std::is_same<LhloOpTy, std::false_type>::value>>
static Value map(XlaOpTy op, ArrayRef<Type> result_types,
static Value map(HloOpTy op, ArrayRef<Type> result_types,
ArrayRef<Value> args, OpBuilder* b, int i = 0) {
return impl::MapLhloOpToStdScalarOp<LhloOpTy>(op.getLoc(), result_types,
args, b);
@ -505,7 +504,7 @@ struct XlaOpToStdScalarOp {
static Value map(lmhlo::CompareOp op, ArrayRef<Type> result_types,
ArrayRef<Value> args, OpBuilder* b) {
auto comparison_direction = op.comparison_direction();
return impl::MapXlaCompareOpToStdScalarOp<lmhlo::CompareOp>(
return impl::MapCompareOpToStdScalarOp<lmhlo::CompareOp>(
op.getLoc(), comparison_direction, result_types, args, b);
}
@ -516,7 +515,7 @@ struct XlaOpToStdScalarOp {
static Value map(mhlo::CompareOp op, ArrayRef<Type> result_types,
ArrayRef<Value> args, OpBuilder* b) {
auto comparison_direction = op.comparison_direction();
return impl::MapXlaCompareOpToStdScalarOp<lmhlo::CompareOp>(
return impl::MapCompareOpToStdScalarOp<lmhlo::CompareOp>(
op.getLoc(), comparison_direction, result_types, args, b);
}
};
@ -524,4 +523,4 @@ struct XlaOpToStdScalarOp {
} // namespace lmhlo
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_MAP_XLA_TO_SCALAR_OP_H_
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_MAP_LMHLO_TO_SCALAR_OP_H_

6
include/mlir-hlo/Dialect/mhlo/transforms/passes.h
View File

@ -56,7 +56,7 @@ std::unique_ptr<OperationPass<FuncOp>> createTransformUnrankedHloPass();
std::unique_ptr<OperationPass<FuncOp>> createSinkConstantsToControlFlowPass();
// fuse mhlo ops to kLoop/kInput fusion patterns
std::unique_ptr<OperationPass<FuncOp>> createXlaHloFusionPass();
std::unique_ptr<OperationPass<FuncOp>> createMhloFusionPass();
} // namespace mhlo
@ -94,12 +94,12 @@ std::unique_ptr<OperationPass<FuncOp>> createLegalizeLhloToParallelLoopsPass();
} // namespace lmhlo
namespace xla {
namespace hlo {
/// Lowers the standard TanhOp to an approximation that does not use intrinsics.
std::unique_ptr<OperationPass<FuncOp>> createLegalizeTanhToApproximationPass();
} // namespace xla
} // namespace hlo
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_PASSES_H_

6
include/mlir-hlo/Dialect/mhlo/transforms/rewriters.h
View File

@ -38,7 +38,7 @@ void PopulateGeneralDotOpLoweringPatterns(OwningRewritePatternList *patterns,
void PopulateComplexLoweringPatterns(MLIRContext *context,
OwningRewritePatternList *patterns);
void PopulateXlaToStdPatterns(OwningRewritePatternList *patterns,
void PopulateMhloToStdPatterns(OwningRewritePatternList *patterns,
MLIRContext *ctx);
// Collection of rewrite patterns for lowering of HLO to LHLO dialect.
@ -93,14 +93,14 @@ void PopulateLegalizeChloToHloPatterns(MLIRContext *context,
} // namespace chlo
namespace xla {
namespace hlo {
// Populates a pattern that translates the standard TanhOp to an approximation
// that does not use intrinsics.
void PopulateTanhToApproximationPatterns(MLIRContext *context,
OwningRewritePatternList *patterns);
} // namespace xla
} // namespace hlo
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_DIALECT_MHLO_TRANSFORMS_REWRITERS_H_

10
include/mlir-hlo/utils/broadcast_utils.h
View File

@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_XLA_IR_BROADCAST_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_XLA_IR_BROADCAST_UTILS_H_
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_BROADCAST_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_BROADCAST_UTILS_H_
// Utilities relating to implementing HLO broadcasting.
// Note: This file should not depend on any non-MLIR TensorFlow libraries.
@ -27,7 +27,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/Support/LLVM.h"
namespace mlir {
namespace xla {
namespace hlo {
// Checks whether the given operand types and broadcast_dims attr represent a
// legal combination for "numpy" style broadcasting (where 1-dims are prepended
@ -43,7 +43,7 @@ Value ComputeBinaryElementwiseBroadcastingResultExtents(Location loc, Value lhs,
Value rhs,
OpBuilder& builder);
} // namespace xla
} // namespace hlo
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_XLA_IR_BROADCAST_UTILS_H_
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_BROADCAST_UTILS_H_

10
include/mlir-hlo/utils/convert_op_folder.h
View File

@ -13,21 +13,21 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_XLA_CONVERT_OP_FOLDER_H_
#define TENSORFLOW_COMPILER_MLIR_XLA_CONVERT_OP_FOLDER_H_
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_CONVERT_OP_FOLDER_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_CONVERT_OP_FOLDER_H_
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Attributes.h"
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/StandardTypes.h"
namespace mlir {
namespace xla {
namespace hlo {
// Converts the given elements attr to the specified elements type.
// Requires type of the elements and new_type to be either integer or float
// type.
mlir::ElementsAttr ConvertElementsAttr(const mlir::ElementsAttr& elements,
mlir::Type new_type);
} // namespace xla
} // namespace hlo
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_XLA_CONVERT_OP_FOLDER_H_
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_CONVERT_OP_FOLDER_H_

6
include/mlir-hlo/utils/cycle_detector.h
View File

@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_XLA_TRANSFORMS_CYCLE_DETECTOR_H_
#define TENSORFLOW_COMPILER_MLIR_XLA_TRANSFORMS_CYCLE_DETECTOR_H_
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_CYCLE_DETECTOR_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_CYCLE_DETECTOR_H_
#include <vector>
@ -162,4 +162,4 @@ class GraphCycles {
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_XLA_TRANSFORMS_CYCLE_DETECTOR_H_
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_CYCLE_DETECTOR_H_

10
include/mlir-hlo/utils/hlo_utils.h
View File

@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_COMPILER_MLIR_XLA_IR_HLO_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_XLA_IR_HLO_UTILS_H_
#ifndef TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_HLO_UTILS_H_
#define TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_HLO_UTILS_H_
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Attributes.h"
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Builders.h"
@ -23,7 +23,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/TypeUtilities.h"
namespace mlir {
namespace xla {
namespace hlo {
// Computes the broadcast dimensions attr for an elementwise binary operator
// between two ranked tensors.
@ -68,7 +68,7 @@ static ElementsAttr getSplat(Builder* b, Value val, T constant) {
// Requires `ty` to be either FloatType of IntegerType.
DenseElementsAttr GetScalarOfType(Type ty, int64_t raw_value);
} // namespace xla
} // namespace hlo
} // namespace mlir
#endif // TENSORFLOW_COMPILER_MLIR_XLA_IR_HLO_UTILS_H_
#endif // TENSORFLOW_COMPILER_MLIR_HLO_INCLUDE_MLIR_HLO_UTILS_HLO_UTILS_H_

4
lib/Dialect/mhlo/IR/chlo_ops.cc
View File

@ -137,7 +137,7 @@ LogicalResult ReifyBroadcastBinaryOpReturnTypeShapes(
auto broadcast_dimensions = op->getAttr("broadcast_dimensions")
.dyn_cast_or_null<DenseIntElementsAttr>();
if (broadcast_dimensions &&
!xla::IsLegalNumpyRankedBroadcast(lhs, rhs, broadcast_dimensions)) {
!hlo::IsLegalNumpyRankedBroadcast(lhs, rhs, broadcast_dimensions)) {
// Note: It is unclear whether the general specification of explicit
// broadcast_dimensions on binary ops is a feature we want to carry
// forward. While it can technically be implemented for ranked-dynamic,
@ -150,7 +150,7 @@ LogicalResult ReifyBroadcastBinaryOpReturnTypeShapes(
<< "broadcast_dimensions = " << broadcast_dimensions;
}
Value computed_shape = xla::ComputeBinaryElementwiseBroadcastingResultExtents(
Value computed_shape = hlo::ComputeBinaryElementwiseBroadcastingResultExtents(
loc, lhs, rhs, builder);
if (!computed_shape) return failure();
reifiedReturnShapes.push_back(computed_shape);

2
lib/Dialect/mhlo/IR/dialect_registration.cc
View File

@ -17,7 +17,7 @@ limitations under the License.
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h"
// Static initialization for XLA dialect registration.
// Static initialization for *HLO dialects registration.
static mlir::DialectRegistration<mlir::mhlo::MhloDialect> mhlo_ops;
static mlir::DialectRegistration<mlir::chlo::HloClientDialect> chlo_ops;
static mlir::DialectRegistration<mlir::lmhlo::LmhloDialect> lmhlo_ops;

6
lib/Dialect/mhlo/IR/hlo_ops.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file defines the operations used in the XLA dialect.
// This file defines the operations used in the MHLO dialect.
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
@ -404,7 +404,7 @@ OpFoldResult ConvertOp::fold(ArrayRef<Attribute> operands) {
// If the operand is constant, we can do the conversion now.
if (auto elementsAttr = operands.front().dyn_cast_or_null<ElementsAttr>()) {
return xla::ConvertElementsAttr(elementsAttr,
return hlo::ConvertElementsAttr(elementsAttr,
getElementTypeOrSelf(getResult()));
}
@ -2135,8 +2135,6 @@ MhloDialect::MhloDialect(MLIRContext* context)
>();
addInterfaces<HLOInlinerInterface>();
addTypes<TokenType>();
// Support unknown operations because not all XLA operations are registered.
// allowUnknownOperations();
}
Type MhloDialect::parseType(DialectAsmParser& parser) const {

2
lib/Dialect/mhlo/IR/lhlo_ops.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file defines the operations used in the XLA dialect.
// This file defines the operations used in the LMHLO dialect.
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h"

4
lib/Dialect/mhlo/transforms/chlo_legalize_to_hlo.cc
View File

@ -96,7 +96,7 @@ struct ConvertRankedDynamicBroadcastBinaryOp
// Check for "numpy"-style rank broadcast.
auto broadcast_dimensions = op.broadcast_dimensions();
if (broadcast_dimensions &&
!xla::IsLegalNumpyRankedBroadcast(lhs, rhs, *broadcast_dimensions)) {
!hlo::IsLegalNumpyRankedBroadcast(lhs, rhs, *broadcast_dimensions)) {
// Note: It is unclear whether the general specification of explicit
// broadcast_dimensions on binary ops is a feature we want to carry
// forward. While it can technically be implemented for ranked-dynamic,
@ -126,7 +126,7 @@ struct ConvertRankedDynamicBroadcastBinaryOp
int64_t result_rank = std::max(lhs_type.getRank(), rhs_type.getRank());
Value result_extents =
xla::ComputeBinaryElementwiseBroadcastingResultExtents(loc, lhs, rhs,
hlo::ComputeBinaryElementwiseBroadcastingResultExtents(loc, lhs, rhs,
rewriter);
// Note that we unconditionally emit DynamicBroadcastInDim ops and let

2
lib/Dialect/mhlo/transforms/chlo_legalize_to_hlo_pass.cc
View File

@ -53,5 +53,5 @@ struct TestChloLegalizeToHloPass
} // namespace mlir
static mlir::PassRegistration<mlir::chlo::TestChloLegalizeToHloPass> pass(
"test-xla-chlo-legalize-to-hlo",
"mhlo-test-chlo-legalize-to-hlo",
"Test pass for applying chlo -> hlo legalization patterns");

10
lib/Dialect/mhlo/transforms/legalize_control_flow.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file implements logic for lowering XLA dialect to Standard dialect.
// This file implements logic for lowering MHLO dialect to Standard dialect.
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/STLExtras.h"
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/StringSwitch.h"
@ -107,8 +107,8 @@ LogicalResult LowerIfOp(mlir::mhlo::IfOp if_op) {
}
LogicalResult LowerWhileOp(mlir::mhlo::WhileOp while_op) {
// Converts an XLA while loop into control flow. This generates a set of MLIR
// blocks and branches, along with inlining the regions provided by the XLA
// Converts a MHLO while loop into control flow. This generates a set of MLIR
// blocks and branches, along with inlining the regions provided by the MHLO
// while loop. The structure should be similar to below:
//
// <prior operations>
@ -232,5 +232,5 @@ mlir::mhlo::createLegalizeControlFlowPass() {
}
static PassRegistration<mlir::mhlo::LegalizeControlFlow> legalize_cf_pass(
"xla-legalize-control-flow",
"Legalize from XLA control flow to MLIR control flow");
"mhlo-legalize-control-flow",
"Legalize from MHLO control flow to CFG control flow");

6
lib/Dialect/mhlo/transforms/legalize_tanh_to_approximation.cc
View File

@ -24,7 +24,7 @@ limitations under the License.
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
namespace mlir {
namespace xla {
namespace hlo {
namespace {
/// Emits the fast tanh approximation that is also used by XLA.
@ -149,8 +149,8 @@ void PopulateTanhToApproximationPatterns(mlir::MLIRContext *context,
}
static PassRegistration<LegalizeTanhToApproximation> legalize_pass(
"xla-legalize-tanh-to-approximation",
"mhlo-legalize-tanh-to-approximation",
"Legalize tanh from standard dialect to an approximation");
} // namespace xla
} // namespace hlo
} // namespace mlir

28
lib/Dialect/mhlo/transforms/xla_legalize_to_linalg.cc → lib/Dialect/mhlo/transforms/legalize_to_linalg.cc
View File

@ -32,7 +32,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/Transforms/DialectConversion.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_xla_to_scalar_op.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_lmhlo_to_scalar_op.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
namespace mlir {
@ -49,12 +49,12 @@ Value getResultValue(Operation* op) {
}
template <bool isLHLO = true>
ShapedType getXLAOpResultType(Operation* op) {
ShapedType getHloOpResultType(Operation* op) {
return getResultValue<isLHLO>(op).getType().template cast<ShapedType>();
}
template <bool isLHLO = true>
bool verifyXLAOpBufferOrTensorSemantics(Operation* op) {
bool verifyHloOpBufferOrTensorSemantics(Operation* op) {
auto verifyType = [&](Value val) -> bool {
return (isLHLO && val.getType().isa<MemRefType>()) ||
(!isLHLO && val.getType().isa<RankedTensorType>());
@ -133,7 +133,7 @@ class PointwiseToLinalgConverter : public OpConversionPattern<OpTy> {
[&](OpBuilder& nestedBuilder, Location nestedLoc, ValueRange args) {
// TODO(ravishankarm) : For now use the method in lmhlo namespace.
// That method needs to be moved out of there.
Value opResult = lmhlo::XlaOpToStdScalarOp::map<OpTy>(
Value opResult = lmhlo::HloOpToStdScalarOp::map<OpTy>(
op, bodyResultTypes,
llvm::to_vector<2>(args.take_front(args_count)), &rewriter);
nestedBuilder.create<linalg::YieldOp>(loc, opResult);
@ -163,7 +163,7 @@ class ScalarPointwiseToStandardConverter : public OpConversionPattern<LhloOp> {
auto lhs = rewriter.create<LoadOp>(loc, lhlo_op.lhs());
auto rhs = rewriter.create<LoadOp>(loc, lhlo_op.rhs());
// TODO(ravishankarm) : Move this method out of lmhlo namespace.
Value opResult = lmhlo::XlaOpToStdScalarOp::map<LhloOp>(
Value opResult = lmhlo::HloOpToStdScalarOp::map<LhloOp>(
lhlo_op, argType.getElementType(), llvm::ArrayRef<Value>{lhs, rhs},
&rewriter);
rewriter.create<StoreOp>(loc, opResult, lhlo_op.out());
@ -274,7 +274,7 @@ struct ConvToLinalgConverter : public OpConversionPattern<lmhlo::ConvOp> {
}
};
/// Base class for lowering xla operations that have one operand and one result,
/// Base class for lowering HLO operations that have one operand and one result,
/// and are semantically equivalent to a copy of the input to the output (like
/// transpose, some reshape, etc.). The derived classes need to provide a method
/// `getIndexingMaps` that returns AffineMaps for the index maps of the input
@ -287,8 +287,8 @@ class DataMovementOpConverter : public OpConversionPattern<OpTy> {
LogicalResult matchAndRewrite(
OpTy op, ArrayRef<Value> args,
ConversionPatternRewriter& rewriter) const final {
if (!verifyXLAOpBufferOrTensorSemantics<isLHLO>(op)) return failure();
auto resultType = getXLAOpResultType<isLHLO>(op);
if (!verifyHloOpBufferOrTensorSemantics<isLHLO>(op)) return failure();
auto resultType = getHloOpResultType<isLHLO>(op);
SmallVector<AffineMap, 2> indexing_maps =
Derived::getIndexingMaps(op, &rewriter);
@ -322,7 +322,7 @@ class BroadcastConverter
ShapedType inputType =
broadcastOp.operand().getType().template cast<ShapedType>();
unsigned inputRank = inputType.getRank();
unsigned nloops = getXLAOpResultType<isLHLO>(broadcastOp).getRank();
unsigned nloops = getHloOpResultType<isLHLO>(broadcastOp).getRank();
// BroadcastOp prepends the dimensions in the `broadcast_sizes` attribute to
// the input's dimensions.
@ -356,7 +356,7 @@ class HloBroadcastInDimConverter
static SmallVector<AffineMap, 2> getIndexingMaps(
mhlo::BroadcastInDimOp broadcastOp, Builder* b) {
auto resultType = getXLAOpResultType<false>(broadcastOp);
auto resultType = getHloOpResultType<false>(broadcastOp);
auto operandType =
broadcastOp.operand().getType().template cast<ShapedType>();
unsigned nloops = resultType.getRank();
@ -555,7 +555,7 @@ class TransposeConverter
isLHLO>::DataMovementOpConverter;
static SmallVector<AffineMap, 2> getIndexingMaps(OpTy op, Builder* b) {
auto resultType =
getXLAOpResultType<isLHLO>(op).template cast<ShapedType>();
getHloOpResultType<isLHLO>(op).template cast<ShapedType>();
auto nloops = resultType.getRank();
SmallVector<AffineExpr, 2> inputExprs;
inputExprs.resize(resultType.getRank());
@ -579,11 +579,11 @@ class ReshapeOpConverter : public OpConversionPattern<OpTy> {
LogicalResult matchAndRewrite(
OpTy reshapeOp, ArrayRef<Value> args,
ConversionPatternRewriter& rewriter) const final {
if (!verifyXLAOpBufferOrTensorSemantics<isLHLO>(reshapeOp))
if (!verifyHloOpBufferOrTensorSemantics<isLHLO>(reshapeOp))
return failure();
ShapedType operandType =
reshapeOp.operand().getType().template cast<ShapedType>();
ShapedType resultType = getXLAOpResultType<isLHLO>(reshapeOp);
ShapedType resultType = getHloOpResultType<isLHLO>(reshapeOp);
if (!operandType.hasStaticShape() || !resultType.hasStaticShape())
return failure();
@ -708,7 +708,7 @@ class ReverseConverter
isLHLO>::DataMovementOpConverter;
static SmallVector<AffineMap, 2> getIndexingMaps(OpTy op, Builder* b) {
auto resultType =
getXLAOpResultType<isLHLO>(op).template cast<ShapedType>();
getHloOpResultType<isLHLO>(op).template cast<ShapedType>();
auto nloops = resultType.getRank();
SmallVector<AffineExpr, 2> inputExprs;
inputExprs.reserve(nloops);

8
lib/Dialect/mhlo/transforms/legalize_to_standard.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file implements logic for lowering XLA dialect to Standard dialect.
// This file implements logic for lowering MHLO dialect to Standard dialect.
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/StringSwitch.h"
#include "third_party/llvm/llvm-project/mlir/include/mlir/Dialect/StandardOps/IR/Ops.h"
@ -187,7 +187,7 @@ std::unique_ptr<mlir::OperationPass<mlir::FuncOp>> createLegalizeToStdPass() {
return std::make_unique<LegalizeToStandard>();
}
void PopulateXlaToStdPatterns(OwningRewritePatternList *patterns,
void PopulateMhloToStdPatterns(OwningRewritePatternList *patterns,
mlir::MLIRContext *ctx) {
mlir::populateWithGenerated(ctx, patterns);
patterns->insert<CompareFConvert, CompareIConvert, ConvertIotaOp>(ctx);
@ -196,12 +196,12 @@ void PopulateXlaToStdPatterns(OwningRewritePatternList *patterns,
/// Perform the lowering to standard dialect.
void LegalizeToStandard::runOnFunction() {
OwningRewritePatternList patterns;
mlir::mhlo::PopulateXlaToStdPatterns(&patterns, &getContext());
mlir::mhlo::PopulateMhloToStdPatterns(&patterns, &getContext());
applyPatternsAndFoldGreedily(getFunction(), patterns);
}
static PassRegistration<LegalizeToStandard> legalize_pass(
"xla-legalize-to-std", "Legalize from XLA dialect to standard dialect");
"mhlo-legalize-to-std", "Legalize from MHLO dialect to standard dialect");
} // end namespace mhlo
} // end namespace mlir

2
lib/Dialect/mhlo/transforms/legalize_to_standard_patterns.td
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This is the legalization pattern definition file for XLA to StandardOps.
// This is the legalization pattern definition file for MHLO to StandardOps.
include "third_party/llvm/llvm-project/mlir/include/mlir/IR/OpBase.td"
include "third_party/llvm/llvm-project/mlir/include/mlir/Dialect/StandardOps/IR/Ops.td"

6
lib/Dialect/mhlo/transforms/lhlo_legalize_to_affine.cc
View File

@ -25,7 +25,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/StandardTypes.h"
#include "third_party/llvm/llvm-project/mlir/include/mlir/Pass/Pass.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_xla_to_scalar_op.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_lmhlo_to_scalar_op.h"
namespace mlir {
namespace lmhlo {
@ -69,7 +69,7 @@ struct DotOpConverter : public OpRewritePattern<DotOp> {
auto r = builder.create<AffineLoadOp>(loc, rhs, rhs_indices);
auto result =
rewriter.create<AffineLoadOp>(loc, op.output(), result_indices);
Value op_result = lmhlo::XlaOpToStdScalarOp::map<DotOp>(
Value op_result = lmhlo::HloOpToStdScalarOp::map<DotOp>(
op, element_type, {l, r, result}, &builder);
map_status = success(op_result != nullptr);
if (failed(map_status)) return;
@ -108,7 +108,7 @@ struct BinaryOpConverter : public OpRewritePattern<LhloOpTy> {
ValueRange induction_vars) {
auto l = builder.create<AffineLoadOp>(loc, lhs, induction_vars);
auto r = builder.create<AffineLoadOp>(loc, rhs, induction_vars);
Value op_result = lmhlo::XlaOpToStdScalarOp::map<LhloOpTy>(
Value op_result = lmhlo::HloOpToStdScalarOp::map<LhloOpTy>(
op, element_type, {l, r}, &builder);
map_status = success(op_result != nullptr);
if (failed(map_status)) return;

2
lib/Dialect/mhlo/transforms/lhlo_legalize_to_gpu.cc
View File

@ -35,7 +35,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/Pass/Pass.h"
#include "third_party/llvm/llvm-project/mlir/include/mlir/Transforms/DialectConversion.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_xla_to_scalar_op.h"
#include "third_party/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/map_lmhlo_to_scalar_op.h"
namespace mlir {
namespace lmhlo {

79
lib/Dialect/mhlo/transforms/lhlo_legalize_to_parallel_loops.cc
View File

@ -192,22 +192,22 @@ class ReduceOpConverter : public OpConversionPattern<lmhlo::ReduceOp> {
using OpConversionPattern<lmhlo::ReduceOp>::OpConversionPattern;
LogicalResult matchAndRewrite(
lmhlo::ReduceOp xla_reduce_op, ArrayRef<Value> /*args*/,
lmhlo::ReduceOp reduce_op, ArrayRef<Value> /*args*/,
ConversionPatternRewriter& rewriter) const final {
// TODO(b/137624192) Implement variadic reduce.
if (xla_reduce_op.out().size() != 1) return failure();
if (reduce_op.out().size() != 1) return failure();
scf::ReduceOp reduce_op =
CreateReduceOpInNestedParallelLoops(xla_reduce_op, &rewriter);
ConvertToReductionOperator(xla_reduce_op.getLoc(), reduce_op,
&xla_reduce_op.body().front(), &rewriter);
rewriter.replaceOp(xla_reduce_op, llvm::None);
scf::ReduceOp scf_reduce_op =
CreateReduceOpInNestedParallelLoops(reduce_op, &rewriter);
ConvertToReductionOperator(reduce_op.getLoc(), scf_reduce_op,
&reduce_op.body().front(), &rewriter);
rewriter.replaceOp(reduce_op, llvm::None);
return success();
}
private:
// Creates nested `scf.parallel` ops with `scf.reduce`. The outer ParallelOp
// refers to the parallel dimensions of `xla_reduce_op` if any and the inner
// refers to the parallel dimensions of `reduce_op` if any and the inner
// ParallelOp refers to the reduction dimensions. The scf.reduce op is
// returned.
//
@ -226,16 +226,15 @@ class ReduceOpConverter : public OpConversionPattern<lmhlo::ReduceOp> {
// scf.yield
// }
scf::ReduceOp CreateReduceOpInNestedParallelLoops(
lmhlo::ReduceOp xla_reduce_op,
ConversionPatternRewriter* rewriter) const {
auto loc = xla_reduce_op.getLoc();
lmhlo::ReduceOp reduce_op, ConversionPatternRewriter* rewriter) const {
auto loc = reduce_op.getLoc();
DenseSet<int> reducing_dims;
for (const auto& rdim : xla_reduce_op.dimensions().getIntValues()) {
for (const auto& rdim : reduce_op.dimensions().getIntValues()) {
reducing_dims.insert(rdim.getSExtValue());
}
Value operand = *xla_reduce_op.operands().begin();
Value out = *xla_reduce_op.out().begin();
Value operand = *reduce_op.operands().begin();
Value out = *reduce_op.out().begin();
SmallVector<Value, 2> parallel_lower, parallel_upper, parallel_step;
SmallVector<Value, 2> reduce_lower, reduce_upper, reduce_step;
auto operand_shape = operand.getType().cast<MemRefType>().getShape();
@ -252,7 +251,7 @@ class ReduceOpConverter : public OpConversionPattern<lmhlo::ReduceOp> {
}
// Load initial value from memref<element_type>.
SmallVector<Value, 1> init_value = {
rewriter->create<LoadOp>(loc, *xla_reduce_op.init_values().begin())};
rewriter->create<LoadOp>(loc, *reduce_op.init_values().begin())};
// Outer ParallelOp is not needed if it is a reduction across all dims.
scf::ParallelOp outer;
if (!parallel_lower.empty()) {
@ -293,7 +292,7 @@ class ReduceOpConverter : public OpConversionPattern<lmhlo::ReduceOp> {
rewriter->setInsertionPointToStart(inner.getBody());
Value elem = rewriter->create<mlir::LoadOp>(
loc, *xla_reduce_op.operands().begin(), indices);
loc, *reduce_op.operands().begin(), indices);
return rewriter->create<scf::ReduceOp>(loc, elem);
}
};
@ -364,42 +363,42 @@ class ReduceWindowOpConverter
using OpConversionPattern<lmhlo::ReduceWindowOp>::OpConversionPattern;
LogicalResult matchAndRewrite(
lmhlo::ReduceWindowOp xla_reduce_window_op, ArrayRef<Value> /*args*/,
lmhlo::ReduceWindowOp reduce_window_op, ArrayRef<Value> /*args*/,
ConversionPatternRewriter& rewriter) const final {
scf::ParallelOp output_loop, window_loop;
std::tie(output_loop, window_loop) =
CreateParallelLoopsToTraverseOutputAndWindow(xla_reduce_window_op,
CreateParallelLoopsToTraverseOutputAndWindow(reduce_window_op,
&rewriter);
scf::ReduceOp reduce_op = CreateReduceOpInNestedParallelLoops(
xla_reduce_window_op, output_loop, window_loop, &rewriter);
reduce_window_op, output_loop, window_loop, &rewriter);
ConvertToReductionOperator(xla_reduce_window_op.getLoc(), reduce_op,
&xla_reduce_window_op.body().front(), &rewriter);
rewriter.replaceOp(xla_reduce_window_op, llvm::None);
ConvertToReductionOperator(reduce_window_op.getLoc(), reduce_op,
&reduce_window_op.body().front(), &rewriter);
rewriter.replaceOp(reduce_window_op, llvm::None);
return success();
}
private:
std::pair<scf::ParallelOp, scf::ParallelOp>
CreateParallelLoopsToTraverseOutputAndWindow(
lmhlo::ReduceWindowOp xla_reduce_window_op,
lmhlo::ReduceWindowOp reduce_window_op,
ConversionPatternRewriter* rewriter) const {
auto loc = xla_reduce_window_op.getLoc();
auto loc = reduce_window_op.getLoc();
Value init_value =
rewriter->create<LoadOp>(loc, xla_reduce_window_op.init_value());
rewriter->create<LoadOp>(loc, reduce_window_op.init_value());
Value zero = rewriter->create<ConstantIndexOp>(loc, 0);
Value one = rewriter->create<ConstantIndexOp>(loc, 1);
// Create an outer parallel loop that spans the output of ReduceWindowOp.
Value xla_output = xla_reduce_window_op.out();
auto output_loop = MakeLoopOverShape(loc, xla_output, rewriter);
Value output = reduce_window_op.out();
auto output_loop = MakeLoopOverShape(loc, output, rewriter);
// Create a nested loop that traverses the window.
SmallVector<Value, 2> window_lower, window_upper, window_step;
rewriter->setInsertionPointToStart(output_loop.getBody());
for (const auto& window_dim : xla_reduce_window_op.window_dimensions()) {
for (const auto& window_dim : reduce_window_op.window_dimensions()) {
window_step.push_back(one);
window_lower.push_back(zero);
window_upper.push_back(
@ -410,38 +409,38 @@ class ReduceWindowOpConverter
Value reduction_result = *window_loop.getResults().begin();
auto output_ivs = output_loop.getInductionVars();
rewriter->create<StoreOp>(loc, reduction_result, xla_output, output_ivs);
rewriter->create<StoreOp>(loc, reduction_result, output, output_ivs);
return std::make_pair(output_loop, window_loop);
}
scf::ReduceOp CreateReduceOpInNestedParallelLoops(
lmhlo::ReduceWindowOp xla_reduce_window_op, scf::ParallelOp output_loop,
lmhlo::ReduceWindowOp reduce_window_op, scf::ParallelOp output_loop,
scf::ParallelOp window_loop, ConversionPatternRewriter* rewriter) const {
rewriter->setInsertionPointToStart(window_loop.getBody());
auto loc = xla_reduce_window_op.getLoc();
auto loc = reduce_window_op.getLoc();
if (xla_reduce_window_op.base_dilations().hasValue() ||
xla_reduce_window_op.window_dilations().hasValue()) {
xla_reduce_window_op.emitRemark(
if (reduce_window_op.base_dilations().hasValue() ||
reduce_window_op.window_dilations().hasValue()) {
reduce_window_op.emitRemark(
"Lowering to parallel loops does not support `base_dilations` or "
"`window_dilations` attributes yet. The attributes will be ignored.");
}
Value xla_operand = xla_reduce_window_op.operand();
auto xla_operand_type = xla_operand.getType().cast<MemRefType>();
Value operand = reduce_window_op.operand();
auto operand_type = operand.getType().cast<MemRefType>();
// Compute ivs in 'arg' buffer and whether these ivs are in pad area or not.
MappedIvs mapped_ivs = MapWindowIvsToInput(
xla_reduce_window_op, output_loop.getInductionVars(),
MappedIvs mapped_ivs =
MapWindowIvsToInput(reduce_window_op, output_loop.getInductionVars(),
window_loop.getInductionVars(), rewriter);
auto elem_or_init = rewriter->create<scf::IfOp>(
loc, xla_operand_type.getElementType(), mapped_ivs.in_bounds,
loc, operand_type.getElementType(), mapped_ivs.in_bounds,
/*withElseRegion=*/true);
OpBuilder then_builder = elem_or_init.getThenBodyBuilder();
Value elem = then_builder.create<mlir::LoadOp>(
loc, xla_reduce_window_op.operand(), mapped_ivs.ivs);
loc, reduce_window_op.operand(), mapped_ivs.ivs);
then_builder.create<scf::YieldOp>(loc, elem);
OpBuilder else_builder = elem_or_init.getElseBodyBuilder();

10
lib/Dialect/mhlo/transforms/lower_complex.cc
View File

@ -45,13 +45,13 @@ class LowerComplex : public PassWrapper<LowerComplex, FunctionPass> {
public:
explicit LowerComplex() : PassWrapper<LowerComplex, FunctionPass>() {}
/// Performs the lowering to XLA dialect.
/// Performs the lowering to MHLO dialect.
void runOnFunction() override;
};
} // end anonymous namespace
namespace mlir {
namespace xla {
namespace hlo {
namespace {
#include "third_party/tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/generated_lower_complex.inc"
@ -62,18 +62,18 @@ void PopulateComplexLoweringPatterns(MLIRContext* context,
OwningRewritePatternList* patterns) {
populateWithGenerated(context, patterns);
}
} // end namespace xla
} // end namespace hlo
} // end namespace mlir
// Lowers the complex operations that can be represented using other operations.
void LowerComplex::runOnFunction() {
// Add lowering patterns to the list.
OwningRewritePatternList patterns;
mlir::xla::PopulateComplexLoweringPatterns(&getContext(), &patterns);
mlir::hlo::PopulateComplexLoweringPatterns(&getContext(), &patterns);
applyPatternsAndFoldGreedily(getFunction(), patterns);
}
static PassRegistration<LowerComplex> pass(
"test-xla-lower-complex",
"mhlo-test-lower-complex",
"Lower complex operations into non-complex operations");

4
lib/Dialect/mhlo/transforms/lower_general_dot.cc
View File

@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// This file implements logic for lowering XLA general dot to a regular dot.
// This file implements logic for lowering MHLO general dot to a regular dot.
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/STLExtras.h"
#include "third_party/llvm/llvm-project/llvm/include/llvm/ADT/StringSwitch.h"
@ -188,5 +188,5 @@ void mlir::mhlo::PopulateGeneralDotOpLoweringPatterns(
}
static PassRegistration<LegalizeGeneralDot> legalize_pass(
"test-xla-lower-general-dot",
"mhlo-test-lower-general-dot",
"Tests lowering general dot to a non-batched dot when possible");

2
lib/Dialect/mhlo/transforms/materialize_broadcasts_pass.cc
View File

@ -54,5 +54,5 @@ struct TestMaterializeBroadcastsPass
} // namespace mlir
static mlir::PassRegistration<mlir::mhlo::TestMaterializeBroadcastsPass> pass(
"test-xla-materialize-broadcasts",
"mhlo-test-materialize-broadcasts",
"Test pass for materializing 'broadcast_dimensions' attributes");

10
lib/Dialect/mhlo/transforms/mhlo_fusion.cc
View File

@ -479,7 +479,7 @@ class FusionPlanner {
EquivalenceClasses<int32_t> leader_for_node_;
};
struct XlaHloFusion : public mlir::PassWrapper<XlaHloFusion, FunctionPass> {
struct MhloFusion : public mlir::PassWrapper<MhloFusion, FunctionPass> {
void runOnFunction() override {
FuncOp func = getFunction();
if (!IsTargetFunc(func)) {
@ -568,12 +568,12 @@ struct XlaHloFusion : public mlir::PassWrapper<XlaHloFusion, FunctionPass> {
} // namespace
std::unique_ptr<OperationPass<FuncOp>> createXlaHloFusion() {
return std::make_unique<XlaHloFusion>();
std::unique_ptr<OperationPass<FuncOp>> createMhloFusion() {
return std::make_unique<MhloFusion>();
}
static PassRegistration<XlaHloFusion> mhlo_fusion_pass(
"xla-hlo-fusion", "fuse mhlo ops to kLoop/kInput fusion patterns.");
static PassRegistration<MhloFusion> mhlo_fusion_pass(
"mhlo-fusion", "fuse mhlo ops to kLoop/kInput fusion patterns.");
} // namespace mhlo
} // namespace mlir

2
lib/Dialect/mhlo/transforms/sink_constants_to_control_flow.cc
View File

@ -71,7 +71,7 @@ class SinkConstantsToControlFlow
};
static mlir::PassRegistration<SinkConstantsToControlFlow> pass(
"xla-hlo-sink-constants-to-control-flow",
"mhlo-sink-constants-to-control-flow",
"Sink constants implicitly captured in control flow regions. This is "
"necessary to export to XLA.");

14
lib/Dialect/mhlo/transforms/test_infer_shaped_type_pass.cc
View File

@ -22,12 +22,12 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/Pass/Pass.h"
namespace mlir {
namespace xla {
namespace hlo {
namespace {
struct InferReturnTypeComponentsPattern : public RewritePattern {
InferReturnTypeComponentsPattern(MLIRContext *context)
: RewritePattern("xla_test.get_return_type_components", 1, context) {}
: RewritePattern("mhlo_test.get_return_type_components", 1, context) {}
LogicalResult matchAndRewrite(Operation *op,
PatternRewriter &rewriter) const override {
if (op->getNumOperands() != 1) return failure();
@ -44,7 +44,7 @@ struct InferReturnTypeComponentsPattern : public RewritePattern {
}
// Replace the op with another pass-through op with attributes added.
OperationState state(op->getLoc(), "xla_test.return_type_components",
OperationState state(op->getLoc(), "mhlo_test.return_type_components",
op->getOperands(), op->getResultTypes(),
op->getAttrs());
auto new_op = rewriter.createOperation(state);
@ -65,7 +65,7 @@ struct InferReturnTypeComponentsPattern : public RewritePattern {
struct ReifyReturnTypeShapesPattern : public RewritePattern {
ReifyReturnTypeShapesPattern(MLIRContext *context)
: RewritePattern("xla_test.reify_return_type_shapes", 1, context) {}
: RewritePattern("mhlo_test.reify_return_type_shapes", 1, context) {}
LogicalResult matchAndRewrite(Operation *op,
PatternRewriter &rewriter) const override {
if (op->getNumOperands() != 1) return failure();
@ -92,9 +92,9 @@ struct TestInferShapedTypeMethodsPass
};
} // namespace
} // namespace xla
} // namespace hlo
} // namespace mlir
static mlir::PassRegistration<mlir::xla::TestInferShapedTypeMethodsPass> pass(
"test-xla-infer-shaped-type-methods",
static mlir::PassRegistration<mlir::hlo::TestInferShapedTypeMethodsPass> pass(
"mhlo-test-infer-shaped-type-methods",
"Uses test ops to invoke InferShapedTypeOpInterface methods");

0
lib/Dialect/mhlo/transforms/xla_transform_unranked_hlo.cc → lib/Dialect/mhlo/transforms/transform_unranked_hlo.cc
View File

2
lib/Dialect/mhlo/transforms/unfuse_batch_norm_pass.cc
View File

@ -42,5 +42,5 @@ struct TestUnfuseBatchNormPass
} // namespace mlir
static mlir::PassRegistration<mlir::mhlo::TestUnfuseBatchNormPass> pass(
"test-xla-unfuse-batch-norm",
"mhlo-test-unfuse-batch-norm",
"Test pass for materializing 'broadcast_dimensions' attributes");

4
lib/utils/broadcast_utils.cc
View File

@ -24,7 +24,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/StandardTypes.h"
namespace mlir {
namespace xla {
namespace hlo {
bool IsLegalNumpyRankedBroadcast(Value lhs, Value rhs,
DenseIntElementsAttr broadcast_dims) {
@ -70,5 +70,5 @@ Value ComputeBinaryElementwiseBroadcastingResultExtents(Location loc, Value lhs,
result_shape_v);
}
} // namespace xla
} // namespace hlo
} // namespace mlir

4
lib/utils/convert_op_folder.cc
View File

@ -22,7 +22,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/TypeUtilities.h"
namespace mlir {
namespace xla {
namespace hlo {
mlir::ElementsAttr ConvertElementsAttr(const mlir::ElementsAttr& elements,
mlir::Type new_type) {
@ -82,5 +82,5 @@ mlir::ElementsAttr ConvertElementsAttr(const mlir::ElementsAttr& elements,
}));
}
} // namespace xla
} // namespace hlo
} // namespace mlir

4
lib/utils/hlo_utils.cc
View File

@ -20,7 +20,7 @@ limitations under the License.
#include "third_party/llvm/llvm-project/mlir/include/mlir/IR/Attributes.h"
namespace mlir {
namespace xla {
namespace hlo {
DenseIntElementsAttr getBroadcastDimensionsAttr(Builder *b, Value x, Value y,
bool allow_empty) {
@ -66,5 +66,5 @@ DenseElementsAttr GetScalarOfType(Type ty, int64_t raw_value) {
return DenseElementsAttr::get(scalar_ty, value);
}
} // namespace xla
} // namespace hlo
} // namespace mlir

20
tests/chlo_infer_shape_type_methods.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -test-xla-infer-shaped-type-methods -allow-unregistered-dialect -split-input-file -verify-diagnostics %s -o - | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-test-infer-shaped-type-methods -allow-unregistered-dialect -split-input-file -verify-diagnostics %s -o - | FileCheck %s
// CHECK-LABEL: @broadcast_add
// Note that all broadcast_ops are expanded from the same template, so
@ -12,7 +12,7 @@ func @broadcast_add(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<1xinde
// CHECK: %[[EXTENTS:.+]] = shape.to_extent_tensor %[[BCAST_S]]
// CHECK: return %[[EXTENTS]]
%0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%1 = "xla_test.reify_return_type_shapes"(%0) : (tensor<?xf32>) -> tensor<1xindex>
%1 = "mhlo_test.reify_return_type_shapes"(%0) : (tensor<?xf32>) -> tensor<1xindex>
return %1 : tensor<1xindex>
}
@ -20,8 +20,8 @@ func @broadcast_add(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<1xinde
// CHECK-LABEL: @complex_ranked_components
func @complex_ranked_components(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xcomplex<f32>> {
%0 = chlo.broadcast_complex %arg0, %arg1 : (tensor<?xf32>, tensor<?x?xf32>) -> tensor<?x?xcomplex<f32>>
// CHECK: "xla_test.return_type_components"(%0) {dims0 = [-1, -1], element_type0 = complex<f32>}
%1 = "xla_test.get_return_type_components"(%0) : (tensor<?x?xcomplex<f32>>) -> tensor<?x?xcomplex<f32>>
// CHECK: "mhlo_test.return_type_components"(%0) {dims0 = [-1, -1], element_type0 = complex<f32>}
%1 = "mhlo_test.get_return_type_components"(%0) : (tensor<?x?xcomplex<f32>>) -> tensor<?x?xcomplex<f32>>
return %1 : tensor<?x?xcomplex<f32>>
}
@ -29,8 +29,8 @@ func @complex_ranked_components(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) ->
// CHECK-LABEL: @compare_ranked_components
func @compare_ranked_components(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xi1> {
%0 = chlo.broadcast_compare %arg0, %arg1 {comparison_direction = "EQ"} : (tensor<?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: "xla_test.return_type_components"(%0) {dims0 = [-1, -1], element_type0 = i1}
%1 = "xla_test.get_return_type_components"(%0) : (tensor<?x?xi1>) -> tensor<?x?xi1>
// CHECK: "mhlo_test.return_type_components"(%0) {dims0 = [-1, -1], element_type0 = i1}
%1 = "mhlo_test.get_return_type_components"(%0) : (tensor<?x?xi1>) -> tensor<?x?xi1>
return %0 : tensor<?x?xi1>
}
@ -38,8 +38,8 @@ func @compare_ranked_components(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) ->
// CHECK-LABEL: @broadcast_add_ranked_components_r1
func @broadcast_add_ranked_components_r1(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
%0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
// CHECK: "xla_test.return_type_components"(%0) {dims0 = [-1], element_type0 = f32}
%1 = "xla_test.get_return_type_components"(%0) : (tensor<?xf32>) -> tensor<?xf32>
// CHECK: "mhlo_test.return_type_components"(%0) {dims0 = [-1], element_type0 = f32}
%1 = "mhlo_test.get_return_type_components"(%0) : (tensor<?xf32>) -> tensor<?xf32>
return %1 : tensor<?xf32>
}
@ -49,8 +49,8 @@ func @broadcast_add_ranked_components_r1x2(%arg0: tensor<?xf32>, %arg1: tensor<?
%0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?x3xf32>) -> tensor<?x3xf32>
// TODO: Overly broad shapes are being returned. Tighten the calculation
// and update/extend these tests.
// CHECK: "xla_test.return_type_components"(%0) {dims0 = [-1, -1], element_type0 = f32}
%1 = "xla_test.get_return_type_components"(%0) : (tensor<?x3xf32>) -> tensor<?x3xf32>
// CHECK: "mhlo_test.return_type_components"(%0) {dims0 = [-1, -1], element_type0 = f32}
%1 = "mhlo_test.get_return_type_components"(%0) : (tensor<?x3xf32>) -> tensor<?x3xf32>
return %1 : tensor<?x3xf32>
}

2
tests/chlo_legalize_to_hlo_broadcasts.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -test-xla-chlo-legalize-to-hlo -cse -split-input-file -verify-diagnostics %s -o - | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-test-chlo-legalize-to-hlo -cse -split-input-file -verify-diagnostics %s -o - | FileCheck %s
// Check the non-broadcast case for each registered op, then just check a
// representative op for detailed broadcast semantics.

2
tests/legalize-control-flow.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -xla-legalize-control-flow %s -o - | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-legalize-control-flow %s -o - | FileCheck %s
// CHECK-LABEL: func @while(%arg0: tensor<i64>) -> tensor<i64> {
func @while(%arg0: tensor<i64>) -> tensor<i64> {

2
tests/legalize-to-std.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -xla-legalize-to-std %s -o - | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-legalize-to-std %s -o - | FileCheck %s
// CHECK-LABEL: func @binary_ops_float(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
func @binary_ops_float(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {

2
tests/legalize_tanh_to_approximation.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -xla-legalize-tanh-to-approximation -split-input-file %s | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-legalize-tanh-to-approximation -split-input-file %s | FileCheck %s
func @tanh_f64(%arg0 : f64) -> f64 {
%res = tanh %arg0 : f64

2
tests/lhlo-legalize-select-and-scatter.mlir
View File

@ -1,6 +1,6 @@
// GenericAtomicRMWOp should contain only ops with no side effects.
// Unfortunately, the legalization pattern for SelectAndScatterOp has to adapt
// to XLA LHLO dialect using allocs/deallocs inside of GenericAtomicRMWOp body.
// to LMHLO dialect using allocs/deallocs inside of GenericAtomicRMWOp body.
// Lowering to STD dialect and store forwarding pass would be required to get
// rid of them. This is exactly what is done in the real MLIR GPU pipeline, but
// here we disable verification with `verify-each=0` to check the output IR.

2
tests/lower-complex.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt %s -test-xla-chlo-legalize-to-hlo -test-xla-lower-complex | FileCheck %s
// RUN: mlir-hlo-opt %s -mhlo-test-chlo-legalize-to-hlo -mhlo-test-lower-complex | FileCheck %s
// CHECK-LABEL: @add
func @add(%arg0 : tensor<2xf32>, %arg1 : tensor<2xf32>, %arg2 : tensor<2xf32>, %arg3 : tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>) {

2
tests/lower-general-dot.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -test-xla-lower-general-dot -split-input-file %s -o - | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-test-lower-general-dot -split-input-file %s -o - | FileCheck %s
// CHECK-LABEL: @testDebatch1
func @testDebatch1(%arg0: tensor<1x1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x1x3xf32> {

2
tests/materialize-broadcasts.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -test-xla-materialize-broadcasts -split-input-file %s -o - | FileCheck %s
// RUN: mlir-hlo-opt -mhlo-test-materialize-broadcasts -split-input-file %s -o - | FileCheck %s
// CHECK-LABEL: @clampBroadcast
// CHECK-SAME: (%[[MIN:.+]]: tensor<f32>, %[[VAL:.+]]: tensor<4xf32>, %[[MAX:.+]]: tensor<f32>)

2
tests/xla-hlo-fusion.mlir → tests/mhlo-fusion.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt %s -xla-hlo-fusion -split-input-file | FileCheck %s
// RUN: mlir-hlo-opt %s -mhlo-fusion -split-input-file | FileCheck %s
// CHECK-LABEL: func @multi_outputs_same
func @multi_outputs_same(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> (tensor<?x?xf32>, tensor<?x?xf32>) {

0
tests/xla-transform-unranked-hlo.mlir → tests/mhlo-transform-unranked.mlir
View File

2
tests/sink-constants-to-control-flow.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt %s -xla-hlo-sink-constants-to-control-flow | FileCheck %s
// RUN: mlir-hlo-opt %s -mhlo-sink-constants-to-control-flow | FileCheck %s
// Tests sinking constants to a while loop.

2
tests/unfuse_batch_norm.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-hlo-opt -split-input-file -test-xla-unfuse-batch-norm -verify-diagnostics %s | FileCheck --enable-var-scope %s
// RUN: mlir-hlo-opt -split-input-file -mhlo-test-unfuse-batch-norm -verify-diagnostics %s | FileCheck --enable-var-scope %s
// CHECK-LABEL: @batchNormInference_2D_inner_features
// CHECK-SAME: %[[X:[^:[:space:]]+]]