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

Use OpState::operator->() to get to member functions in Operation so we can remove the corresponding methods from OpState.

Browse Source 
PiperOrigin-RevId: 347322042
Change-Id: I02b32db7aba1c33e4dd5510a294fcfff7b122d60
This commit is contained in:
Christian Sigg 2020-12-13 22:58:40 -08:00 committed by TensorFlower Gardener
parent 1e8c13a7f0
commit 9697081dac
14 changed files with 49 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
tensorflow/compiler/mlir/lite/quantization/quantization_driver.cc
View File

@ -532,7 +532,7 @@ void QuantizationDriver::QuantizeValue(Value value, QuantParams params,
// quantization pass. These ops can be removed without losing original
// program accuracy.
// TODO(fengliuai): make the attribute being part of op definition.
quantize.setAttr(kVolatileOpAttrName, builder_.getUnitAttr());
quantize->setAttr(kVolatileOpAttrName, builder_.getUnitAttr());
// `original_result` has a use to `quantize`, so this will replace that use
// by the result of `dequantize`. Remember to reset that use afterwards

6
tensorflow/compiler/mlir/lite/utils/lstm_utils.cc
View File

@ -438,7 +438,7 @@ LogicalResult ConvertLSTMCellSimpleToFusedLSTM::RewriteFunc() {
}
LogicalResult ConvertLSTMCellSimpleToFusedLSTM::InitializeFromFuncAttributes() {
auto attr = fused_func_op_.getAttrOfType<StringAttr>(kTFImplements);
auto attr = fused_func_op_->getAttrOfType<StringAttr>(kTFImplements);
if (!attr) {
return fused_func_op_.emitError()
<< "Invalid function attribute, expected " << kTFImplements
@ -639,7 +639,7 @@ LogicalResult ConvertKerasLSTMLayer(mlir::FuncOp func_op, OpBuilder* builder) {
// TFL lstm only supports time-majored inputs, so if it's not time-majored,
// we will transpose the inputs and outputs.
auto time_major_attr = func_op.getAttrOfType<BoolAttr>("tf.time_major");
auto time_major_attr = func_op->getAttrOfType<BoolAttr>("tf.time_major");
if (time_major_attr == nullptr) return failure();
bool time_majored = time_major_attr.getValue();
@ -654,7 +654,7 @@ LogicalResult ConvertKerasLSTMLayer(mlir::FuncOp func_op, OpBuilder* builder) {
// Handle go_backwards:
// LSTM in Keras semantic will reverse the input sequence if it's go_backwards
auto go_backwards_attr = func_op.getAttrOfType<BoolAttr>("tf.go_backwards");
auto go_backwards_attr = func_op->getAttrOfType<BoolAttr>("tf.go_backwards");
if (go_backwards_attr != nullptr && go_backwards_attr.getValue()) {
int time_dim = time_majored ? 0 : 1;

21
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
View File

@ -1479,9 +1479,10 @@ LogicalResult Conv2DOp::UpdateDataFormat(StringRef data_format) {
if (failed(::mlir::TF::UpdateDataFormat(data_format, this))) return failure();
// Update convolution attributes.
setAttr("dilations", ShuffleArrayAttr(dilations(), perm));
setAttr("strides", ShuffleArrayAttr(strides(), perm));
setAttr("explicit_paddings", ShuffleArrayAttr(explicit_paddings(), perm, 2));
(*this)->setAttr("dilations", ShuffleArrayAttr(dilations(), perm));
(*this)->setAttr("strides", ShuffleArrayAttr(strides(), perm));
(*this)->setAttr("explicit_paddings",
ShuffleArrayAttr(explicit_paddings(), perm, 2));
return success();
}
@ -1553,9 +1554,10 @@ LogicalResult Conv2DBackpropFilterOp::UpdateDataFormat(StringRef data_format) {
if (failed(::mlir::TF::UpdateDataFormat(data_format, this))) return failure();
// Update convolution attributes.
setAttr("dilations", ShuffleArrayAttr(dilations(), perm));
setAttr("strides", ShuffleArrayAttr(strides(), perm));
setAttr("explicit_paddings", ShuffleArrayAttr(explicit_paddings(), perm, 2));
(*this)->setAttr("dilations", ShuffleArrayAttr(dilations(), perm));
(*this)->setAttr("strides", ShuffleArrayAttr(strides(), perm));
(*this)->setAttr("explicit_paddings",
ShuffleArrayAttr(explicit_paddings(), perm, 2));
// Permute filter sizes operand.
OpBuilder builder(getOperation());
@ -1618,9 +1620,10 @@ LogicalResult Conv2DBackpropInputOp::UpdateDataFormat(StringRef data_format) {
if (failed(::mlir::TF::UpdateDataFormat(data_format, this))) return failure();
// Update convolution attributes.
setAttr("dilations", ShuffleArrayAttr(dilations(), perm));
setAttr("strides", ShuffleArrayAttr(strides(), perm));
setAttr("explicit_paddings", ShuffleArrayAttr(explicit_paddings(), perm, 2));
(*this)->setAttr("dilations", ShuffleArrayAttr(dilations(), perm));
(*this)->setAttr("strides", ShuffleArrayAttr(strides(), perm));
(*this)->setAttr("explicit_paddings",
ShuffleArrayAttr(explicit_paddings(), perm, 2));
// Permute input sizes operand.
OpBuilder builder(getOperation());

6
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_helpers.inc
View File

@ -370,7 +370,7 @@ LogicalResult UpdateDataFormat(StringRef data_format, Op *op) {
if (perm.empty()) return failure();
// Update data format attribute.
op->setAttr("data_format", StringAttr::get(data_format, op->getContext()));
(*op)->setAttr("data_format", StringAttr::get(data_format, op->getContext()));
// Update types for all layout sensitive results.
auto layout_sensitive = cast<LayoutSensitiveInterface>(op->getOperation());
@ -421,12 +421,12 @@ LogicalResult FoldOperandsPermutation(
GetDataFormatPermutation(op->data_format(), target_data_format);
if (reverse_permutation.empty()) return failure();
op->setAttr("data_format", StringAttr::get(target_data_format, context));
(*op)->setAttr("data_format", StringAttr::get(target_data_format, context));
for (auto pair : shuffle_attrs) {
StringRef attr_name = pair.first;
ArrayAttr attr_value = pair.second;
op->setAttr(attr_name, ShuffleArrayAttr(attr_value, reverse_permutation));
(*op)->setAttr(attr_name, ShuffleArrayAttr(attr_value, reverse_permutation));
}
auto fold = cast<FoldOperandsTransposeInterface>(op->getOperation());

2
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
View File

@ -401,7 +401,7 @@ static LogicalResult Verify(ParseExampleV2Op op) {
template <class OpClass>
static LogicalResult VerifyPartitionedCall(OpClass op) {
auto module = op->template getParentOfType<ModuleOp>();
SymbolRefAttr func = op.getAttr("f").template cast<SymbolRefAttr>();
SymbolRefAttr func = op->getAttr("f").template cast<SymbolRefAttr>();
auto function =
dyn_cast_or_null<FuncOp>(SymbolTable::lookupSymbolIn(module, func));

8
tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.cc
View File

@ -342,7 +342,7 @@ LogicalResult VerifyExportedFunc(FuncOp func) {
continue;
}
if (func.getArgAttr(i, "tf.resource_name")) {
if (module.getAttr("tf_saved_model.under_construction")) continue;
if (module->getAttr("tf_saved_model.under_construction")) continue;
return func.emitError() << "'tf.resource_name' attribute is not allowed "
"unless it is being under construction";
}
@ -355,7 +355,7 @@ LogicalResult VerifyExportedFunc(FuncOp func) {
if (auto attr = func.getArgAttrOfType<FlatSymbolRefAttr>(
i, "tf_saved_model.bound_input")) {
if (!unique_bound_inputs.insert(attr.getValue()).second) {
if (module.getAttr("tf_saved_model.under_construction")) continue;
if (module->getAttr("tf_saved_model.under_construction")) continue;
return func.emitError()
<< "duplicate 'tf_saved_model.bound_input' binding";
}
@ -431,7 +431,7 @@ bool IsExported(Operation *op) {
}
bool HasTfSavedModelSemantics(ModuleOp module) {
return module.getAttr("tf_saved_model.semantics") != nullptr;
return module->getAttr("tf_saved_model.semantics") != nullptr;
}
Operation *LookupBoundInput(FuncOp func, int arg_index,
@ -483,7 +483,7 @@ class OptimizeSessionInitializerPattern
if (to_keep.empty())
rewriter.eraseOp(op);
else
op.setAttr("initializers", rewriter.getArrayAttr(to_keep));
op->setAttr("initializers", rewriter.getArrayAttr(to_keep));
return success();
}

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

@ -3064,7 +3064,7 @@ Status CreateSavedModelIR(
/*executor_type=*/builder.getStringAttr(""));
body_builder.create<mlir::ReturnOp>(func.getLoc(), call.getResults());
}
func.setAttr(
func->setAttr(
"tf_saved_model.exported_names",
builder.getStrArrayAttr(object_names.GetExportedNames(node_id)));
const SavedConcreteFunction& concrete_function =
@ -3162,7 +3162,7 @@ Status CreateSavedModelIR(
value_attr,
/*type=*/mlir::TypeAttr::get(type),
/*is_mutable=*/builder.getUnitAttr());
op.setAttr(
op->setAttr(
"tf_saved_model.exported_names",
builder.getStrArrayAttr(object_names.GetExportedNames(node_id)));
} else if (object.kind_case() == SavedObject::kConstant) {
@ -3182,13 +3182,13 @@ Status CreateSavedModelIR(
value_attr,
/*type=*/mlir::TypeAttr::get(value_attr.Attribute::getType()),
/*is_mutable=*/nullptr);
op.setAttr(
op->setAttr(
"tf_saved_model.exported_names",
builder.getStrArrayAttr(object_names.GetExportedNames(node_id)));
}
}
AdjustBoundInputArgTypes(module);
module.setAttr("tf_saved_model.semantics", builder.getUnitAttr());
module->setAttr("tf_saved_model.semantics", builder.getUnitAttr());
SortSavedModelModule(module);
MarkSavedModelFunctionVisibility(module);
return Status::OK();
@ -3448,7 +3448,7 @@ Status SavedModelSignatureDefImporterLite::ConvertInitializer(
// Set the exported name of init function to an reserved name for
// tf_saved_model.
init_func_op.setAttr(
init_func_op->setAttr(
"tf_saved_model.exported_names",
builder.getStrArrayAttr({absl::StrCat(
"__tf_saved_model_session_initializer_", target_node_name)}));
@ -3508,8 +3508,8 @@ Status SavedModelSignatureDefImporterLite::ConvertSignature(
<< sig_def_key << ".";
// Use unique SignatureDef key as exported name.
func_op.setAttr("tf_saved_model.exported_names",
builder.getStrArrayAttr({sig_def_key}));
func_op->setAttr("tf_saved_model.exported_names",
builder.getStrArrayAttr({sig_def_key}));
// Transfer input and output parameter names to index_path attributes.
for (auto input_and_idx : llvm::enumerate(inputs)) {
@ -3623,7 +3623,7 @@ SavedModelSignatureDefImporterLite::ConvertSignatures() {
builder.create<mlir::tf_saved_model::SessionInitializerOp>(
module_->getLoc(), builder.getArrayAttr(init_sym_refs));
module_->setAttr("tf_saved_model.semantics", builder.getUnitAttr());
(*module_)->setAttr("tf_saved_model.semantics", builder.getUnitAttr());
SortSavedModelModule(*module_);
MarkSavedModelFunctionVisibility(*module_);
@ -3653,7 +3653,8 @@ class SavedModelSignatureDefImporter {
context, upgrade_legacy, /*import_restore=*/false));
mlir::OpBuilder builder(module->getContext());
module->setAttr("tf_saved_model.under_construction", builder.getUnitAttr());
(*module)->setAttr("tf_saved_model.under_construction",
builder.getUnitAttr());
TF_RETURN_IF_ERROR(LiftVariables(bundle, *module));
module->removeAttr("tf_saved_model.under_construction");

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

@ -30,9 +30,9 @@ void PopulateTfVersions(mlir::ModuleOp module, const VersionDef& versions) {
"bad_consumers",
b.getI32ArrayAttr(llvm::ArrayRef<int32_t>(
versions.bad_consumers().begin(), versions.bad_consumers().end())));
module.setAttr("tf.versions",
b.getDictionaryAttr(llvm::ArrayRef<mlir::NamedAttribute>(
{producer, min_consumer, bad_consumers})));
module->setAttr("tf.versions",
b.getDictionaryAttr(llvm::ArrayRef<mlir::NamedAttribute>(
{producer, min_consumer, bad_consumers})));
}
mlir::LogicalResult ExtractTfVersions(mlir::ModuleOp module,

5
tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.cc
View File

@ -92,8 +92,9 @@ mlir::LogicalResult CreateSplitOp(const int num_split,
llvm::SmallVector<mlir::Type, 4> output_types(num_split, output_type);
*split_op = builder->create<mlir::TF::SplitOp>(
location, output_types, split_dimension_op.output(), src_input);
split_op->setAttr(kNumSplitAttr, builder->getIntegerAttr(
builder->getIntegerType(32), num_split));
(*split_op)->setAttr(
kNumSplitAttr,
builder->getIntegerAttr(builder->getIntegerType(32), num_split));
return mlir::success();
}

4
tensorflow/compiler/mlir/tfr/ir/tfr_ops.cc
View File

@ -231,7 +231,7 @@ static LogicalResult Verify(TFRFuncOp func) {
// Collect all the undefined attributes used in the inputs.
llvm::SmallVector<StringAttr, 4> undefined_attrs;
for (auto attr : input_used_attrs) {
if (!func.getAttr(attr.getValue())) {
if (!func->getAttr(attr.getValue())) {
undefined_attrs.push_back(attr);
}
}
@ -295,7 +295,7 @@ static LogicalResult Verify(TFRFuncOp func) {
// Collect all the undefined attributes used in the outputs.
for (auto attr : output_used_attrs) {
if (!func.getAttr(attr.getValue())) {
if (!func->getAttr(attr.getValue())) {
undefined_attrs.push_back(attr);
}
}

4
tensorflow/compiler/mlir/tfr/passes/decompose.cc
View File

@ -111,7 +111,7 @@ LogicalResult DecomposeTFOpsPass::RewriteUnregisteredTFOps() {
FuncOp func = getFunction();
SymbolTable table(external_tfr_module.hasValue()
? *external_tfr_module
: func.getParentOfType<ModuleOp>());
: func->getParentOfType<ModuleOp>());
OpBuilder builder(func);
bool changed = false;
func.walk([&table, &builder, &changed](Operation* op) {
@ -244,7 +244,7 @@ LogicalResult DecomposeTFOpsPass::InlineTFRFuncCalls() {
FuncOp func = getFunction();
SymbolTable table(external_tfr_module.hasValue()
? *external_tfr_module
: func.getParentOfType<ModuleOp>());
: func->getParentOfType<ModuleOp>());
// The inliner only inlines the TFR call op.
bool changed = false;

2
tensorflow/compiler/mlir/tfr/passes/raise_to_tf.cc
View File

@ -450,7 +450,7 @@ void RaiseToTFOpsPass::runOnFunction() {
MLIRContext* ctx = &getContext();
SymbolTable table(external_tfr_module.hasValue()
? *external_tfr_module
: func.getParentOfType<ModuleOp>());
: func->getParentOfType<ModuleOp>());
OwningRewritePatternList patterns;
patterns.insert<RewriteTFRCallOp>(ctx, table, materialize_derived_attrs);

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

@ -142,7 +142,7 @@ LogicalResult CopyAllowedUnregisteredAttrs(Operation* src, CallOp dst,
// Unregistered attribute.
if (GetAllowedAttributes().contains(attr_name)) {
dst.setAttr(attr.first, attr.second);
dst->setAttr(attr.first, attr.second);
} else {
src->emitError("Denied unregistered attribute was found: " + attr_name);
return failure();

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

@ -4637,11 +4637,11 @@ class ConvertInfeedDequeueTupleOp
if (sharding_proto.type() == ::xla::OpSharding::TUPLE) {
*sharding_proto.add_tuple_shardings() =
::xla::sharding_builder::AssignDevice(0);
data_and_token.setAttr(
data_and_token->setAttr(
kShardingAttr,
rewriter.getStringAttr(sharding_proto.SerializeAsString()));
} else {
data_and_token.setAttr(kShardingAttr, op._XlaShardingAttr());
data_and_token->setAttr(kShardingAttr, op._XlaShardingAttr());
}
}
@ -5157,7 +5157,7 @@ class ConvertXlaShardingOp : public OpRewritePattern<TF::XlaShardingOp> {
/*call_target_name=*/rewriter.getStringAttr("Sharding"),
/*has_side_effect=*/rewriter.getBoolAttr(false),
/*backend_config=*/rewriter.getStringAttr(""));
custom_call.setAttr(kShardingAttr, op._XlaShardingAttr());
custom_call->setAttr(kShardingAttr, op._XlaShardingAttr());
rewriter.replaceOp(op, custom_call.getResult(0));
return success();