experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Bump OSS LLVM to f6bb58542aca5959acd1ab2e6ec757570df534e2

Browse Source 
PiperOrigin-RevId: 292146195
Change-Id: If859953eaba0ee672abf79ae44612839f74471af
This commit is contained in:
Benjamin Kramer 2020-01-29 08:23:51 -08:00 committed by TensorFlower Gardener
parent 642708d212
commit b1171f57ad
26 changed files with 76 additions and 78 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
tensorflow/compiler/mlir/lite/flatbuffer_translate.cc
View File

@ -523,7 +523,7 @@ class Translator {
};
std::string Translator::UniqueName(mlir::Value val) {
return name_mapper_.GetUniqueName(val);
return std::string(name_mapper_.GetUniqueName(val));
}
Optional<BufferOffset<tflite::Buffer>> Translator::BuildBuffer(
@ -1089,7 +1089,7 @@ Optional<BufferOffset<tflite::SubGraph>> Translator::BuildSubGraph(FuncOp fn) {
for (unsigned i = 0, e = bb.getNumArguments(); i < e; ++i) {
mlir::BlockArgument arg = bb.getArgument(i);
std::string name;
if (has_input_attr) name = name_mapper_.GetUniqueName(arg);
if (has_input_attr) name = std::string(name_mapper_.GetUniqueName(arg));
if (name.empty()) name = absl::StrCat("arg", i);
if (!build_tensor_and_buffer(arg, name)) return llvm::None;
}

3
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
View File

@ -797,8 +797,7 @@ struct RemoveAdjacentReshape : public RewritePattern {
// With
// %2 = "tfl.reshape"(%0, %shape1)
rewriter.replaceOpWithNewOp<ReshapeOp>(
{prevOp.getResult()}, op, thisOp.getType(), prevOp.getOperand(0),
thisOp.getOperand(1));
op, thisOp.getType(), prevOp.getOperand(0), thisOp.getOperand(1));
}
};

2
tensorflow/compiler/mlir/lite/operator_converter_gen.cc
View File

@ -122,7 +122,7 @@ static void EmitOptionBuilders(const RecordKeeper &record_keeper,
os << formatv(
" auto {0} = Convert{1}ForOptionWriter(op.{0}(), fbb);\n",
val.getName(), record->getClasses()[0]->getName());
options.push_back(val.getName());
options.push_back(std::string(val.getName()));
}
}
}

2
tensorflow/compiler/mlir/lite/transforms/lower_static_tensor_list.cc
View File

@ -701,7 +701,7 @@ struct ConvertTensorListStack
if ((ranked_type && ranked_type.getRank() == 0) ||
!matchPattern(element_shape, m_Constant(&dense_elem_attr))) {
// If no constant is spotted, just forward the operand.
rewriter.replaceOp(op, {input}, llvm::None);
rewriter.replaceOp(op, {input});
return matchSuccess();
}

4
tensorflow/compiler/mlir/op_or_arg_name_mapper.cc
View File

@ -153,7 +153,7 @@ std::string OpOrArgLocNameMapper::GetName(OpOrVal op_or_val) {
if (!name_from_loc.empty()) return name_from_loc;
// If the location is none of the expected types, then simply use name
// generated using the op type.
return op->getName().getStringRef();
return std::string(op->getName().getStringRef());
}
auto val = op_or_val.dyn_cast<mlir::Value>();
auto name_from_loc = GetNameFromLoc(val.getLoc());
@ -166,7 +166,7 @@ std::string OpOrArgLocNameMapper::GetName(OpOrVal op_or_val) {
return llvm::formatv("{0}:{1}",
result.getOwner()->getName().getStringRef(),
result.getResultNumber());
return result.getOwner()->getName().getStringRef();
return std::string(result.getOwner()->getName().getStringRef());
}
return "";
}

3
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc
View File

@ -2680,7 +2680,6 @@ static LogicalResult Verify(WhileOp op) {
return op.emitOpError("requires cond function to have exactly one result");
SmallVector<Type, 4> operands(op.getOperandTypes());
SmallVector<Type, 4> results(op.getResultTypes());
// Collect all the type lists for the op so that different pairs of type lists
// can be compared for the compatibility.
@ -2688,7 +2687,7 @@ static LogicalResult Verify(WhileOp op) {
std::pair<std::string, ArrayRef<Type>> typeLists[] = {
{"operand", operands},
{"body function result", bodyFuncType.getResults()},
{"result", results},
{"result", op.getResultTypes()},
{"cond function input", condFuncType.getInputs()},
{"body function input", bodyFuncType.getInputs()},
};

6
tensorflow/compiler/mlir/tensorflow/transforms/cluster_outlining.cc
View File

@ -58,10 +58,8 @@ FuncOp BuildFunction(StringRef device, llvm::ArrayRef<Value> live_ins,
operand_types.reserve(live_ins.size());
for (Value v : live_ins) operand_types.emplace_back(v.getType());
llvm::SmallVector<Type, 4> result_types(launch_op.getResultTypes());
auto func_type =
FunctionType::get(operand_types, result_types, builder->getContext());
auto func_type = FunctionType::get(operand_types, launch_op.getResultTypes(),
builder->getContext());
std::string func_name_prefix = Twine(device, "_func").str();
FuncOp outlined_func =

2
tensorflow/compiler/mlir/tensorflow/transforms/materialize_mlir_passthrough_op.cc
View File

@ -44,7 +44,7 @@ void MaterializePassthroughOpPass::runOnFunction() {
getFunction().walk([](Operation *op) {
auto passthrough_op = dyn_cast<TF::MlirPassthroughOp>(op);
if (!passthrough_op) return;
std::string module_string = passthrough_op.mlir_module();
std::string module_string(passthrough_op.mlir_module());
// Parse the module.
auto nested_module = parseSourceString(module_string, op->getContext());
if (!nested_module) {

11
tensorflow/compiler/mlir/tensorflow/transforms/tpu_rewrite_pass.cc
View File

@ -170,7 +170,7 @@ LogicalResult SetMetadataProtoFromLaunchFuncOp(
xla::DebugOptions::STEP_MARK_AT_ENTRY;
if (!step_marker_location.getValue().empty() &&
!xla::DebugOptions::StepMarkerLocation_Parse(
step_marker_location.getValue(), &location))
std::string(step_marker_location.getValue()), &location))
return op.emitOpError(llvm::formatv("bad '{0}' attribute with value '{1}'",
kStepMarkerLocationAttr,
step_marker_location.getValue()));
@ -191,7 +191,7 @@ LogicalResult SetMetadataProtoFromLaunchFuncOp(
tensorflow::tpu::PaddingMap* padding =
metadata->mutable_padding_maps()->Add();
if (!padding->ParseFromString(padding_attr_str.getValue()))
if (!padding->ParseFromString(std::string(padding_attr_str.getValue())))
return op.emitOpError(llvm::formatv(
"bad '{0}' attribute at index {1} with value '{2}'", kPaddingMapAttr,
padding_and_idx.index(), padding_attr_str.getValue()));
@ -339,10 +339,9 @@ Operation* BuildExecuteOp(Operation* compile_op,
// follow-up CLs.
// TPUExecute has same output types as launch_func.
llvm::SmallVector<Type, 4> output_types(launch_func.getResultTypes());
return builder->create<TF::TPUExecuteOp>(launch_func.getLoc(), output_types,
tensor_inputs,
llvm::ArrayRef<NamedAttribute>{});
return builder->create<TF::TPUExecuteOp>(
launch_func.getLoc(), launch_func.getResultTypes(), tensor_inputs,
llvm::ArrayRef<NamedAttribute>{});
}
// Creates a `tf.TPUCompileSucceededAssert` operation that parses compilation

2
tensorflow/compiler/mlir/tensorflow/transforms/tpu_variable_runtime_reformatting.cc
View File

@ -167,7 +167,7 @@ AnnotateCompileOpAndGetExecuteArgToWhileArgsMapping(
auto metadata_str = compile->getAttrOfType<StringAttr>("metadata");
assert(metadata_str && "Missing compilation metadata");
tensorflow::tpu::TPUCompileMetadataProto metadata;
metadata.ParseFromString(metadata_str.getValue());
metadata.ParseFromString(std::string(metadata_str.getValue()));
int64_t num_replicas = replicate.n().getLimitedValue();
// Find the formattable operands of `execute`, which must be mirrored
// variables (arguments of `replicate`), and must be pass-throughs from while

10
tensorflow/compiler/mlir/tensorflow/translate/export_graphdef.cc
View File

@ -244,7 +244,8 @@ StatusOr<std::unique_ptr<NodeDef>> Exporter::GetArgumentNode(
if (!name.empty())
node_def->set_name(name.str());
else
node_def->set_name(op_to_name_.GetUniqueName(func.getName().str()));
node_def->set_name(
std::string(op_to_name_.GetUniqueName(func.getName().str())));
node_def->set_op(FunctionLibraryDefinition::kArgOp);
@ -282,7 +283,8 @@ StatusOr<std::unique_ptr<NodeDef>> Exporter::GetReturnNode(
if (!name.empty())
node_def->set_name(name.str());
else
node_def->set_name(op_to_name_.GetUniqueName(function.getName().str()));
node_def->set_name(
std::string(op_to_name_.GetUniqueName(function.getName().str())));
node_def->set_op(FunctionLibraryDefinition::kRetOp);
DataType dtype;
@ -579,7 +581,7 @@ StatusOr<std::unique_ptr<Graph>> Exporter::Convert(
// If there is a result index specified, ensure only one and that it
// matches the result index of the op.
auto result = it.value().cast<mlir::OpResult>();
std::string orig_name = output_names[it.index()];
std::string orig_name(output_names[it.index()]);
auto tensor_id = ParseTensorName(orig_name);
auto name = LegalizeNodeName(
llvm::StringRef(tensor_id.node().data(), tensor_id.node().size()));
@ -607,7 +609,7 @@ StatusOr<std::unique_ptr<Graph>> Exporter::Convert(
TF_RET_CHECK(input_names.size() == block.getNumArguments());
for (auto it : llvm::enumerate(function.getArguments())) {
// TODO(lyandy): Update when changing feed/fetch import.
std::string orig_name = input_names[it.index()];
std::string orig_name(input_names[it.index()]);
std::string name = LegalizeNodeName(orig_name);
auto tensor_id = ParseTensorName(name);
TF_RET_CHECK(tensor_id.index() == 0)

2
tensorflow/compiler/mlir/tensorflow/translate/export_tf_dialect_op.cc
View File

@ -101,7 +101,7 @@ Status GetUnregisteredAttrs(
GetTensorFlowOpName(inst->getName().getStringRef()));
const tensorflow::OpRegistrationData* op_reg_data =
tensorflow::OpRegistry::Global()->LookUp(op_name);
tensorflow::OpRegistry::Global()->LookUp(std::string(op_name));
if (!op_reg_data) {
// This is likely a function call node, so we should continue.
return Status::OK();

19
tensorflow/compiler/mlir/tensorflow/translate/import_model.cc
View File

@ -124,7 +124,9 @@ class NameUniquifier : public OpOrArgNameMapper {
: flib_(flib) {}
private:
bool IsUnique(llvm::StringRef name) override { return !flib_.Contains(name); }
bool IsUnique(llvm::StringRef name) override {
return !flib_.Contains(std::string(name));
}
std::string GetName(OpOrVal op_or_val) override {
DCHECK(false) << "Unimplemented";
@ -1047,15 +1049,16 @@ void ImporterBase::GetArgsAndRetsFromFunctionBody(
Status ImporterBase::ConvertLibFunction(llvm::StringRef func_name) {
// If the library function has been converted already, nothing needs to be
// done.
if (tf_name_to_mlir_name_->find(func_name) != tf_name_to_mlir_name_->end())
if (tf_name_to_mlir_name_->find(std::string(func_name)) !=
tf_name_to_mlir_name_->end())
return Status::OK();
std::string mlir_func_name =
function_name_uniquifier_->GetUniqueName(func_name);
(*tf_name_to_mlir_name_)[func_name] = mlir_func_name;
std::string mlir_func_name(
function_name_uniquifier_->GetUniqueName(func_name));
(*tf_name_to_mlir_name_)[std::string(func_name)] = mlir_func_name;
const auto& func_lib = graph_flib_;
const auto* func_def = func_lib.Find(func_name);
const auto* func_def = func_lib.Find(std::string(func_name));
if (func_def == nullptr) {
return errors::FailedPrecondition(
absl::StrCat("Failed to find function '", StringRefToView(func_name),
@ -1089,7 +1092,7 @@ Status ImporterBase::ConvertLibFunction(llvm::StringRef func_name) {
// Checks for an associated custom gradient function. Adds it to the attribute
// list of this function.
auto grad_func_name = func_lib.FindGradient(func_name);
auto grad_func_name = func_lib.FindGradient(std::string(func_name));
if (!grad_func_name.empty()) {
TF_RETURN_IF_ERROR(ConvertLibFunction(grad_func_name));
auto mlir_grad_func_name = (*tf_name_to_mlir_name_)[grad_func_name];
@ -3019,7 +3022,7 @@ Status SavedModelV1Importer::ReadVariablesFromSession(
std::vector<std::string> variable_names;
variable_names.reserve(variable_names_and_ops.size());
for (const auto& name_and_location : variable_names_and_ops)
variable_names.push_back(name_and_location.first);
variable_names.push_back(std::string(name_and_location.first));
std::vector<Tensor> resource_tensors;
TF_RETURN_IF_ERROR(bundle_.GetSession()->Run(

6
tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc
View File

@ -126,8 +126,10 @@ Status CreateFileForDumping(llvm::StringRef name,
<< "' directory for dumping: " << status;
return Status(error::Code::UNAVAILABLE, "(unavailable)");
}
*filepath =
llvm::Twine(dir).concat("/").concat(MakeUniqueFilename(name)).str();
*filepath = llvm::Twine(dir)
.concat("/")
.concat(MakeUniqueFilename(std::string(name)))
.str();
// Try to open the file and generate a raw_ostream.
std::unique_ptr<WritableFile> file;

2
tensorflow/compiler/mlir/tensorflow/utils/eval_util.cc
View File

@ -97,7 +97,7 @@ mlir::LogicalResult EvaluateOperation(
// Builds TF operation and sets all the attributes.
std::string node_name = "unnamed";
if (auto attr = inst->getAttrOfType<mlir::StringAttr>("name")) {
node_name = attr.getValue();
node_name = std::string(attr.getValue());
}
auto node_def_or = ConvertTFDialectOpToNodeDef(
inst, node_name.c_str(), /*ignore_unregistered_attrs=*/true);

8
tensorflow/compiler/mlir/tensorflow/utils/export_utils.cc
View File

@ -136,7 +136,7 @@ Status ConvertAttribute(const mlir::UnitAttr& attr, AttrValue* value) {
}
Status ConvertAttribute(const mlir::FlatSymbolRefAttr& attr, AttrValue* value) {
value->mutable_func()->set_name(attr.getValue());
value->mutable_func()->set_name(std::string(attr.getValue()));
return Status::OK();
}
@ -291,7 +291,7 @@ StatusOr<std::unique_ptr<NodeDef>> GetOperationNodeDef(
}
node_def->set_name(name.str());
node_def->set_op(op_name.str());
node_def->set_op(std::string(op_name.str()));
// Add inputs to the NodeDef based on the number of operands. This is required
// as later when edges are added to the Node using Graph::AddEdge the
@ -300,7 +300,7 @@ StatusOr<std::unique_ptr<NodeDef>> GetOperationNodeDef(
node_def->add_input();
}
if (auto attr = inst->getAttrOfType<mlir::StringAttr>("device")) {
node_def->set_device(attr.getValue());
node_def->set_device(std::string(attr.getValue()));
}
// Add the node attributes.
@ -343,7 +343,7 @@ Status ConvertAttributes(
switch (attr.getKind()) {
case mlir::StandardAttributes::SymbolRef: {
auto func_attr = attr.cast<mlir::FlatSymbolRefAttr>();
value.mutable_func()->set_name(func_attr.getValue());
value.mutable_func()->set_name(std::string(func_attr.getValue()));
func_call_attrs[string(name)] = value;
continue;
}

2
tensorflow/compiler/mlir/xla/hlo_function_importer.cc
View File

@ -58,7 +58,7 @@ namespace {
// direction. Longterm solution is to add a function attribute to maintain the
// original HLO naming.
string SanitizeFunctionName(llvm::StringRef name) {
string output = name;
string output(name);
llvm::for_each(output, [](char& x) { x = x == '-' ? '_' : x; });
return output;
}

23
tensorflow/compiler/mlir/xla/mlir_hlo_to_hlo.cc
View File

@ -124,7 +124,8 @@ static xla::FftType Convert_fft_type(llvm::StringRef fft_type_str) {
xla::FftType fft_type_enum;
// Illegal fft_type string would be caught by the verifier, so 'FftType_Parse'
// call below should never return false.
if (!FftType_Parse(fft_type_str, &fft_type_enum)) return xla::FftType::FFT;
if (!FftType_Parse(std::string(fft_type_str), &fft_type_enum))
return xla::FftType::FFT;
return fft_type_enum;
}
@ -179,7 +180,7 @@ static xla::TriangularSolveOptions::Transpose Convert_transpose_a(
xla::TriangularSolveOptions::Transpose transpose_enum;
// Illegal tanspose string would be caught by the verifier, so
// 'Transpose_Parse' call below should never return false.
if (!xla::TriangularSolveOptions::Transpose_Parse(transpose_str,
if (!xla::TriangularSolveOptions::Transpose_Parse(std::string(transpose_str),
&transpose_enum))
return xla::TriangularSolveOptions::NO_TRANSPOSE;
return transpose_enum;
@ -550,8 +551,8 @@ LogicalResult ExportXlaOp(CustomCallOp op, OpLoweringContext ctx) {
if (op.has_side_effect()) return failure();
auto& value_map = *ctx.values;
value_map[op] = xla::CustomCall(
ctx.builder, op.call_target_name(), GetTuple(op.args(), ctx),
xla::TypeToShape(op.getType()), op.backend_config());
ctx.builder, std::string(op.call_target_name()), GetTuple(op.args(), ctx),
xla::TypeToShape(op.getType()), std::string(op.backend_config()));
return success();
}
@ -560,8 +561,9 @@ LogicalResult ExportXlaOp(InfeedOp op, OpLoweringContext ctx) {
// The shape argument expected by the xla client API is the type of the first
// element in the result tuple.
auto result_type = op.getType().cast<mlir::TupleType>().getType(0);
value_map[op] = xla::InfeedWithToken(
value_map[op.token()], xla::TypeToShape(result_type), op.infeed_config());
value_map[op] =
xla::InfeedWithToken(value_map[op.token()], xla::TypeToShape(result_type),
std::string(op.infeed_config()));
return success();
}
@ -586,9 +588,10 @@ LogicalResult ExportXlaOp(MapOp op, OpLoweringContext ctx) {
LogicalResult ExportXlaOp(OutfeedOp op, OpLoweringContext ctx) {
auto& value_map = *ctx.values;
value_map[op] = xla::OutfeedWithToken(
value_map[op.operand()], value_map[op.token()],
xla::TypeToShape(op.operand().getType()), op.outfeed_config());
value_map[op] =
xla::OutfeedWithToken(value_map[op.operand()], value_map[op.token()],
xla::TypeToShape(op.operand().getType()),
std::string(op.outfeed_config()));
return success();
}
@ -754,7 +757,7 @@ LogicalResult ExportXlaOp(SortOp op, OpLoweringContext ctx) {
LogicalResult ExportXlaOp(TraceOp op, OpLoweringContext ctx) {
auto& value_map = *ctx.values;
xla::Trace(op.tag(), value_map[op.operand()]);
xla::Trace(std::string(op.tag()), value_map[op.operand()]);
return success();
}

2
tensorflow/compiler/mlir/xla/operator_writer_gen.cc
View File

@ -52,7 +52,7 @@ static std::string GetDefaultAttrExport(
return "Convert_" + named_attr.name.str();
}
static std::string GetClientBuilder(const Operator& op) {
static StringRef GetClientBuilder(const Operator& op) {
static const auto* kOpToXLABuilderMap =
new llvm::StringMap<StringRef>{{"ReverseOp", "Rev"},
{"ConcatenateOp", "ConcatInDim"},

8
tensorflow/compiler/mlir/xla/transforms/hlo_legalize_to_lhlo.cc
View File

@ -123,8 +123,7 @@ class HloToLhloOpConverter : public ConversionPattern {
}
rewriter.create<LhloOpTy>(op->getLoc(), llvm::None, buffer_args,
op->getAttrs());
rewriter.replaceOp(op, ArrayRef<Value>(buffer_args).slice(operands.size()),
original_results);
rewriter.replaceOp(op, ArrayRef<Value>(buffer_args).slice(operands.size()));
return matchSuccess();
}
};
@ -178,8 +177,7 @@ struct HloToLHloReduceConverter
rewriter.setInsertionPointToEnd(&entry_block);
rewriter.create<xla_lhlo::TerminatorOp>(loc);
rewriter.replaceOp(op, ArrayRef<Value>(buffer_args).slice(operands.size()),
original_results);
rewriter.replaceOp(op, ArrayRef<Value>(buffer_args).slice(operands.size()));
return matchSuccess();
}
@ -193,7 +191,7 @@ class HloToLhloTensorLoadConverter : public ConversionPattern {
PatternMatchResult matchAndRewrite(
Operation* op, ArrayRef<Value> operands,
ConversionPatternRewriter& rewriter) const final {
rewriter.replaceOp(op, operands, op->getResults());
rewriter.replaceOp(op, operands);
return matchSuccess();
}
};

14
tensorflow/compiler/mlir/xla/transforms/legalize_tf.cc
View File

@ -2040,7 +2040,7 @@ class GenericConvertReductionOp : public OpRewritePattern<OpTy> {
if (op.keep_dims()) {
result = rewriter.create<ReshapeOp>(loc, op.getType(), result);
}
rewriter.replaceOp(op, {result}, {op.reduction_indices()});
rewriter.replaceOp(op, {result});
return this->matchSuccess();
}
@ -2338,7 +2338,7 @@ class ConvertTileOp : public OpRewritePattern<TF::TileOp> {
result = rewriter.create<ReshapeOp>(loc, output_type, result);
}
rewriter.replaceOp(op, {result}, {op.multiples()});
rewriter.replaceOp(op, {result});
return matchSuccess();
}
@ -2385,7 +2385,7 @@ class ConvertMaxPoolGradOp : public OpRewritePattern<TF::MaxPoolGradOp> {
rewriter.create<ReturnOp>(loc, reducer.getResult());
}
rewriter.replaceOp(op, {result}, {op.orig_output()});
rewriter.replaceOp(op, {result});
return matchSuccess();
}
@ -2530,7 +2530,7 @@ class ConvertConv2DBackpropInputOp
/*batch_group_count=*/rewriter.getI64IntegerAttr(1),
/*precision_config=*/ArrayAttr());
rewriter.replaceOp(op, {result}, {op.input_sizes()});
rewriter.replaceOp(op, {result});
return matchSuccess();
}
@ -2732,7 +2732,7 @@ class ConvertConv2DBackpropFilterOp
/*batch_group_count=*/rewriter.getI64IntegerAttr(1),
/*precision_config=*/ArrayAttr());
rewriter.replaceOp(op, {result}, {op.filter_sizes()});
rewriter.replaceOp(op, {result});
return matchSuccess();
}
@ -2784,9 +2784,7 @@ class ConvertOneHotOp : public OpRewritePattern<TF::OneHotOp> {
Value result = rewriter.create<SelectOp>(loc, op.getType(), compare,
on_value, off_value);
rewriter.replaceOp(
op, {result},
{op.indices(), op.on_value(), op.depth(), op.off_value()});
rewriter.replaceOp(op, {result});
return matchSuccess();
}

6
tensorflow/compiler/mlir/xla/transforms/legalize_tf_control_flow.cc
View File

@ -115,8 +115,7 @@ void LowerIf(TF::IfOp op, ModuleOp module) {
// Create the new conditional op with tuple inputs.
SmallVector<Value, 3> operands(op.getOperands());
SmallVector<Type, 4> types(op.getResultTypes());
auto result_type = builder.getTupleType(types);
auto result_type = builder.getTupleType(op.getResultTypes());
auto conditional = builder.create<xla_hlo::ConditionalOp>(
loc, result_type, op.cond(), tuple_input, tuple_input);
@ -147,9 +146,8 @@ void LowerWhile(TF::WhileOp op, ModuleOp module) {
// Create the new while op with tuple inputs.
SmallVector<Value, 3> operands(op.getOperands());
SmallVector<Type, 4> types(op.getResultTypes());
auto while_op = builder.create<xla_hlo::WhileOp>(
loc, builder.getTupleType(types), tuple_input);
loc, builder.getTupleType(op.getResultTypes()), tuple_input);
// Import the regions for both the cond and body. These regions must be
// updated to tuple the return results together and use the xla hlo return op.

6
tensorflow/compiler/xla/service/cpu/simple_orc_jit.cc
View File

@ -57,7 +57,7 @@ llvm::SmallVector<std::string, 0> DetectMachineAttributes() {
if (llvm::sys::getHostCPUFeatures(host_features)) {
for (auto& feature : host_features) {
if (feature.second) {
result.push_back(feature.first());
result.push_back(std::string(feature.first()));
}
}
}
@ -93,8 +93,8 @@ SimpleOrcJIT::SimpleOrcJIT(
data_layout_(target_machine_->createDataLayout()),
symbol_resolver_(llvm::orc::createLegacyLookupResolver(
execution_session_,
[this](const std::string& name) -> llvm::JITSymbol {
return this->ResolveRuntimeSymbol(name);
[this](llvm::StringRef name) -> llvm::JITSymbol {
return this->ResolveRuntimeSymbol(std::string(name));
},
[](llvm::Error Err) {
cantFail(std::move(Err), "lookupFlags failed");

2
tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc
View File

@ -1514,7 +1514,7 @@ std::unique_ptr<KernelThunk> IrEmitterUnnested::BuildKernelThunk(
}
return absl::make_unique<KernelThunk>(
non_constant_buffers, kernel->getName(),
non_constant_buffers, std::string(kernel->getName()),
implements_whole_instruction ? inst : nullptr, unroll_factor);
}

4
tensorflow/workspace.bzl
View File

@ -595,8 +595,8 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
)
# Check out LLVM and MLIR from llvm-project.
LLVM_COMMIT = "c8695ba9cdebfc25af3312a84d91ae6f0f98487b"
LLVM_SHA256 = "d11de654ff4d374a3807788a30c62224ff5ac6299dd10d5e0afaafc4776fbc6c"
LLVM_COMMIT = "f6bb58542aca5959acd1ab2e6ec757570df534e2"
LLVM_SHA256 = "158ee622595aa1775a3ba99da2e74cc35ffbb292e6ff215e6fa022e4218322f7"
LLVM_URLS = [
"https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/{commit}.tar.gz".format(commit = LLVM_COMMIT),
"https://github.com/llvm/llvm-project/archive/{commit}.tar.gz".format(commit = LLVM_COMMIT),

1
third_party/mlir/BUILD vendored
View File

@ -2279,7 +2279,6 @@ cc_library(
"include/mlir/Dialect/Linalg/EDSC/Intrinsics.h",
"include/mlir/Dialect/Linalg/Passes.h",
"include/mlir/Dialect/Linalg/Transforms/LinalgTransforms.h",
"include/mlir/Dialect/Linalg/Utils/Intrinsics.h",
"include/mlir/Dialect/Linalg/Utils/Utils.h",
],
includes = ["include"],