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[-Wsign-compare] warning fixes batch 6

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This commit is contained in:
Tare Gaskin 2020-06-29 00:13:20 +00:00
parent 6dbeb8d948
commit 6162dbe55e
15 changed files with 53 additions and 40 deletions
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3
tensorflow/compiler/tf2xla/ops/xla_ops.cc
View File

@ -441,7 +441,8 @@ REGISTER_OP("XlaReduce")
auto dim_in_range = [rank](int64 dim) {
return dim >= 0 && dim < rank;
};
if (rank < dimensions_to_reduce.size() ||
const int dimensions_to_reduce_size = dimensions_to_reduce.size();
if (rank < dimensions_to_reduce_size ||
dims_set.size() != dimensions_to_reduce.size() ||
!absl::c_all_of(dimensions_to_reduce, dim_in_range)) {
return errors::InvalidArgument(

34
tensorflow/core/framework/shape_inference.cc
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@ -62,14 +62,14 @@ InferenceContext::InferenceContext(
}
std::vector<std::unique_ptr<std::vector<ShapeAndType>>> handle_data(
input_shapes.size());
for (int i = 0; i < input_handle_shapes_and_types.size(); ++i) {
for (int i = 0, iter_limit = input_handle_shapes_and_types.size(); i < iter_limit; ++i) {
const auto& v = input_handle_shapes_and_types[i];
if (v == nullptr) {
continue;
}
handle_data[i].reset(new std::vector<ShapeAndType>(v->size()));
auto& new_v = *handle_data[i];
for (int j = 0; j < v->size(); ++j) {
for (int j = 0, iter_limit = v->size(); j < iter_limit; ++j) {
const auto& p = (*v)[j];
construction_status_.Update(
MakeShapeFromPartialTensorShape(p.first, &new_v[j].shape));
@ -123,7 +123,8 @@ Status InferenceContext::set_output(StringPiece output_name,
} else {
const int start = result->second.first;
const int size = result->second.second - start;
if (size != shapes.size()) {
const int shapes_size = shapes.size();
if (size != shapes_size) {
return errors::InvalidArgument("Must have exactly ", shapes.size(),
" shapes.");
}
@ -181,7 +182,8 @@ void InferenceContext::PreInputInit(
}
Status InferenceContext::ExpandOutputs(int new_output_size) {
if (new_output_size < outputs_.size()) {
int outputs_size_ = outputs_.size();
if (new_output_size < outputs_size_) {
return errors::InvalidArgument("Trying to reduce number of outputs of op.");
}
outputs_.resize(new_output_size, nullptr);
@ -209,8 +211,8 @@ void InferenceContext::PostInputInit(
}
input_handle_shapes_and_types_ = std::move(input_handle_data);
}
if (inputs_.size() != num_inputs_from_node_def) {
int inputs_size_ = inputs_.size();
if (inputs_size_ != num_inputs_from_node_def) {
construction_status_ = errors::InvalidArgument(
"Wrong number of inputs passed: ", inputs_.size(), " while ",
num_inputs_from_node_def, " expected based on NodeDef");
@ -718,7 +720,8 @@ Status InferenceContext::MakeShapeFromShapeTensorTreatScalarAsUnknownShape(
TF_RETURN_IF_ERROR(WithRankAtMost(input(input_idx), 1, &input_shape));
requested_input_tensor_as_partial_shape_[input_idx] = true;
if (input_idx < input_tensors_as_shapes_.size() &&
int input_tensors_as_shapes_size_ = input_tensors_as_shapes_.size();
if (input_idx < input_tensors_as_shapes_size_ &&
input_tensors_as_shapes_[input_idx].IsSet() &&
RankKnown(input_tensors_as_shapes_[input_idx])) {
*out = input_tensors_as_shapes_[input_idx];
@ -736,7 +739,8 @@ Status InferenceContext::MakeShapeFromShapeTensor(int input_idx,
TF_RETURN_IF_ERROR(WithRank(input(input_idx), 1, &input_shape));
requested_input_tensor_as_partial_shape_[input_idx] = true;
if (input_idx < input_tensors_as_shapes_.size() &&
int input_tensors_as_shapes_size_ = input_tensors_as_shapes_.size();
if (input_idx < input_tensors_as_shapes_size_ &&
input_tensors_as_shapes_[input_idx].IsSet() &&
RankKnown(input_tensors_as_shapes_[input_idx])) {
*out = input_tensors_as_shapes_[input_idx];
@ -1099,14 +1103,16 @@ Status InferenceContext::AttachContext(const Status& status) {
std::vector<string> input_from_tensors_str;
std::vector<string> input_from_tensors_as_shape_str;
input_from_tensors_as_shape_str.reserve(inputs_.size());
for (int i = 0; i < inputs_.size(); ++i) {
for (int i = 0, iter_limit = inputs_.size(); i < iter_limit; ++i) {
int input_tensors_size_ = input_tensors_.size();
int input_tensors_as_shapes_size_ = input_tensors_as_shapes_.size();
if (requested_input_tensor_as_partial_shape_[i] &&
i < input_tensors_as_shapes_.size() &&
i < input_tensors_as_shapes_size_ &&
input_tensors_as_shapes_[i].IsSet() &&
RankKnown(input_tensors_as_shapes_[i])) {
input_from_tensors_as_shape_str.push_back(strings::StrCat(
"input[", i, "] = ", DebugString(input_tensors_as_shapes_[i])));
} else if (requested_input_tensor_[i] && i < input_tensors_.size() &&
} else if (requested_input_tensor_[i] && i < input_tensors_size_ &&
input_tensors_[i] != nullptr) {
input_from_tensors_str.push_back(strings::StrCat(
"input[", i, "] = <",
@ -1140,7 +1146,7 @@ bool InferenceContext::MergeHandleShapesAndTypes(
}
std::vector<ShapeAndType> new_values(shapes_and_types.size());
bool refined = false;
for (int i = 0; i < shapes_and_types.size(); ++i) {
for (int i = 0, iter_limit = shapes_and_types.size(); i < iter_limit; ++i) {
const ShapeAndType& existing = (*to_update)[i];
if (shapes_and_types[i].dtype == existing.dtype) {
new_values[i].dtype = existing.dtype;
@ -1164,7 +1170,7 @@ bool InferenceContext::MergeHandleShapesAndTypes(
if (!refined) {
return false;
}
for (int i = 0; i < new_values.size(); ++i) {
for (int i = 0, iter_limit = new_values.size(); i < iter_limit; ++i) {
(*to_update)[i] = new_values[i];
}
return true;
@ -1199,7 +1205,7 @@ bool InferenceContext::RelaxHandleShapesAndMergeTypes(
return false;
}
std::vector<ShapeAndType> new_values(shapes_and_types.size());
for (int i = 0; i < shapes_and_types.size(); ++i) {
for (int i = 0, iter_limit = shapes_and_types.size(); i < iter_limit; ++i) {
const ShapeAndType& existing = (*to_update)[i];
if (shapes_and_types[i].dtype == existing.dtype) {
new_values[i].dtype = existing.dtype;

11
tensorflow/core/grappler/costs/op_level_cost_estimator.cc
View File

@ -1469,9 +1469,10 @@ Costs OpLevelCostEstimator::PredictEinsum(const OpContext& op_context) const {
found_unknown_shapes = a_input_shape_unknown || b_input_shape_unknown ||
(a_input.shape().dim_size() < matrix_rank) ||
(b_input.shape().dim_size() < matrix_rank);
if (a_input_str.size() != a_input_shape.dim_size() ||
b_input_str.size() != b_input_shape.dim_size()) {
int a_input_str_size = a_input_str.size();
int b_input_str_size = b_input_str.size();
if (a_input_str_size != a_input_shape.dim_size() ||
b_input_str_size != b_input_shape.dim_size()) {
VLOG(1) << "Missing accurate estimator for op: " << op_info.op()
<< ", equation subscripts don't match tensor rank.";
return PredictCostOfAnUnknownOp(op_context);
@ -1513,7 +1514,7 @@ Costs OpLevelCostEstimator::PredictEinsum(const OpContext& op_context) const {
n_dim.set_size(1);
k_dim.set_size(1);
for (int i_idx = 0; i_idx < a_input_str.size(); ++i_idx) {
for (int i_idx = 0, iter_limit = a_input_str.size(); i_idx < iter_limit; ++i_idx) {
if (b_input_str.find(a_input_str[i_idx]) == std::string::npos) {
if (rhs_str.find(a_input_str[i_idx]) == std::string::npos) {
VLOG(1) << "Missing accurate estimator for op: " << op_info.op();
@ -1533,7 +1534,7 @@ Costs OpLevelCostEstimator::PredictEinsum(const OpContext& op_context) const {
*(a_matrix_shape->add_dim()) = a_input_shape.dim(i_idx);
*(b_matrix_shape->add_dim()) = a_input_shape.dim(i_idx);
}
for (int i_idx = 0; i_idx < b_input_str.size(); ++i_idx) {
for (int i_idx = 0, iter_limit = b_input_str.size(); i_idx < iter_limit; ++i_idx) {
if (a_input_str.find(b_input_str[i_idx]) == std::string::npos) {
if (rhs_str.find(b_input_str[i_idx]) == std::string::npos) {
VLOG(1) << "Missing accurate estimator for op: " << op_info.op();

2
tensorflow/core/grappler/optimizers/common_subgraph_elimination.cc
View File

@ -73,7 +73,7 @@ class UniqueNodes {
if (it == memoized_signatures_.end()) return;
std::vector<NodeDef*>& candidates = rep_[it->second];
for (int i = 0; i < candidates.size(); ++i) {
for (int i = 0, iter_limit = candidates.size(); i < iter_limit; ++i) {
if (candidates[i] == node) {
std::swap(candidates[i], candidates[candidates.size() - 1]);
candidates.resize(candidates.size() - 1);

2
tensorflow/core/grappler/optimizers/debug_stripper.cc
View File

@ -63,7 +63,7 @@ Status DebugStripper::Optimize(Cluster* cluster, const GrapplerItem& item,
node.mutable_attr()->swap(new_attr);
// As Identity op only takes one input, mark redundant inputs as control
// input.
for (size_t i = 1; i < node.input_size(); ++i) {
for (int i = 1, iter_limit = node.input_size(); i < iter_limit; ++i) {
if (!IsControlInput(node.input(i))) {
*node.mutable_input(i) = AsControlDependency(NodeName(node.input(i)));
}

3
tensorflow/core/grappler/optimizers/model_pruner.cc
View File

@ -401,9 +401,10 @@ Status SplitIdentityNInputs(GraphDef* graph,
}
const int num_non_control_inputs = NumNonControlInputs(*node);
const int terminal_second_size = terminal.second.size();
if (node->attr().count("T") == 0 ||
node->attr().at("T").list().type_size() != num_non_control_inputs ||
terminal.second.size() >= num_non_control_inputs) {
terminal_second_size >= num_non_control_inputs) {
continue;
}

2
tensorflow/core/grappler/utils.cc
View File

@ -357,7 +357,7 @@ void PermuteNodesInPlace(GraphDef* graph, std::vector<int>* permutation,
}
permutation->swap(inv_perm);
}
for (std::size_t n = 0; n + 1 < permutation->size(); ++n) {
for (int n = 0, iter_limit = permutation->size(); n + 1 < iter_limit; ++n) {
while (n != (*permutation)[n]) {
std::size_t r = (*permutation)[n];
graph->mutable_node()->SwapElements(n, r);

6
tensorflow/core/grappler/utils/topological_sort.cc
View File

@ -81,7 +81,8 @@ Status ComputeTopologicalOrder(
int ready_node = (*ready_nodes)[front];
for (int fanout : graph_view.GetFanout(ready_node)) {
++num_ready_inputs[fanout];
if (num_ready_inputs[fanout] == graph_view.GetFanin(fanout).size()) {
int graph_view_GetFanin_fanout_size = graph_view.GetFanin(fanout).size();
if (num_ready_inputs[fanout] == graph_view_GetFanin_fanout_size) {
ready_nodes->push_back(fanout);
++back;
}
@ -95,7 +96,8 @@ Status ComputeTopologicalOrder(
"at node = "
<< graph.node(back).DebugString();
for (int i = 0; i < graph_view.num_nodes(); ++i) {
if (num_ready_inputs[i] != graph_view.GetFanin(i).size()) {
int graph_view_GetFanin_i_size = graph_view.GetFanin(i).size();
if (num_ready_inputs[i] != graph_view_GetFanin_i_size) {
VLOG(1) << "Node not ready: " << graph.node(i).DebugString();
}
}

2
tensorflow/core/kernels/initializable_lookup_table.cc
View File

@ -74,7 +74,7 @@ Status InitializableLookupTable::Initialize(InitTableIterator& iter) {
Status InitializableLookupTable::AreEntriesSame(const InitTableIterator& iter,
bool* result) {
*result = iter.total_size() == size();
*result = static_cast<size_t>(iter.total_size()) == size();
return Status::OK();
}

2
tensorflow/core/kernels/lookup_util.cc
View File

@ -132,7 +132,7 @@ class TextFileLineIterator
std::vector<string> tokens;
if (!ignore_split_) {
tokens = str_util::Split(line, delimiter_);
if (std::max(key_index_, value_index_) >= tokens.size()) {
if ( static_cast<size_t>(std::max(key_index_, value_index_)) >= tokens.size()) {
status_ = errors::InvalidArgument(
"Invalid number of columns in ", filename_, " line ", next_id_,
" (", line, ") : expected ", std::max(key_index_, value_index_),

2
tensorflow/core/profiler/utils/derived_timeline.cc
View File

@ -130,7 +130,7 @@ void DerivedXLineBuilder::ExpandOrAddLevelEvent(const XEvent& event,
}
void DerivedXLineBuilder::ResetLastEvents(int level) {
for (int i = level; i < last_event_by_level_.size(); ++i) {
for (int i = level, iter_limit = last_event_by_level_.size(); i < iter_limit; ++i) {
last_event_by_level_[i] = absl::nullopt;
}
if (level == 0) ResetDependentLines();

2
tensorflow/core/profiler/utils/xplane_utils.cc
View File

@ -155,7 +155,7 @@ void SortXSpace(XSpace* space) {
// smaller than these value.
void NormalizeTimestamps(XPlane* plane, uint64 start_time_ns) {
for (XLine& line : *plane->mutable_lines()) {
if (line.timestamp_ns() >= start_time_ns) {
if (line.timestamp_ns() >= static_cast<int64>(start_time_ns)) {
line.set_timestamp_ns(line.timestamp_ns() - start_time_ns);
}
}

6
tensorflow/core/util/bcast.h
View File

@ -139,7 +139,8 @@ BCastList<N>::BCastList(const BCastList::Vec (&x)[N],
if (x[i] != x[0]) {
all_equal = false;
}
if (x[i].size() > largest_rank) {
int x_i_size = x[i].size();
if (x_i_size > largest_rank) {
largest_rank = x[i].size();
}
}
@ -176,7 +177,8 @@ BCastList<N>::BCastList(const BCastList::Vec (&x)[N],
// 1-extend and align all vectors.
for (int i = 0; i < N; ++i) {
if (copy[i].size() < largest_rank) {
int copy_i_size = copy[i].size();
if (copy_i_size < largest_rank) {
copy[i].resize(largest_rank, 1);
}
}

12
tensorflow/python/framework/python_op_gen.cc
View File

@ -63,7 +63,7 @@ void AddInferredAttr(const string& indentation, const string& attr_name,
string VectorToTuple(const std::vector<string>& l) {
if (l.size() == 1) return strings::StrCat("(", l.front(), ",)");
string ret = "(";
for (int i = 0; i < l.size(); ++i) {
for (int i = 0, iter_limit = l.size(); i < iter_limit; ++i) {
if (i > 0) {
strings::StrAppend(&ret, ", ");
}
@ -75,11 +75,11 @@ string VectorToTuple(const std::vector<string>& l) {
void Unflatten(const string& prefix, const std::vector<string>& output_sizes,
const string& var, string* result) {
for (int i = 0; i < output_sizes.size(); ++i) {
for (int i = 0, iter_limit = output_sizes.size(); i < iter_limit; ++i) {
if (!output_sizes[i].empty()) {
strings::StrAppend(result, prefix, var, " = ");
if (i > 0) strings::StrAppend(result, var, "[:", i, "] + ");
if (i + 1 < output_sizes.size()) {
if (i + 1 < iter_limit) {
// Special case i == 0 to avoid "0 +" in the generated code.
if (i == 0) {
strings::StrAppend(result, "[", var, "[:", output_sizes[i], "]] + ",
@ -295,7 +295,7 @@ string GenEagerPythonOp::Code() {
// from the end of params_no_default_, and adding params_no_default_.
attrs_.reserve(params_no_default_.size() - op_def_.input_arg_size() +
params_with_default_.size());
for (int i = op_def_.input_arg_size(); i < params_no_default_.size(); ++i) {
for (int i = op_def_.input_arg_size(), iter_limit = params_no_default_.size(); i < iter_limit; ++i) {
attrs_.push_back(params_no_default_[i].GetName());
}
for (const auto& p : params_with_default_) {
@ -331,7 +331,7 @@ string GenEagerPythonOp::Code() {
parameters_with_defaults.empty() ? "" : ", ", "name=None");
// Add attr_expressions_ for attrs that are params.
for (int i = 0; i < attrs_.size(); ++i) {
for (int i = 0, iter_limit = attrs_.size(); i < iter_limit; ++i) {
const string& attr_name = attrs_[i];
const string& attr_api_name =
param_names_[i + op_def_.input_arg_size()].GetRenameTo();
@ -522,7 +522,7 @@ bool GenEagerPythonOp::GetEagerFunctionSetup(const string& indentation,
}
}
for (int i = 0; i < attrs_.size(); ++i) {
for (int i = 0, iter_limit = attrs_.size(); i < iter_limit; ++i) {
const string& attr_name = attrs_[i];
const auto& param = param_names_[i + op_def_.input_arg_size()];
const auto& attr = *FindAttr(attr_name, op_def_);

4
tensorflow/python/framework/python_op_gen_internal.cc
View File

@ -561,10 +561,10 @@ string GenPythonOp::Code() {
// from the end of args_no_default, and adding args_no_default.
attrs_.reserve(params_no_default.size() - op_def_.input_arg_size() +
params_with_default.size());
for (int i = op_def_.input_arg_size(); i < params_no_default.size(); ++i) {
for (int i = op_def_.input_arg_size(), iter_limit = params_no_default.size(); i < iter_limit; ++i) {
attrs_.push_back(params_no_default[i].GetName());
}
for (int i = 0; i < params_with_default.size(); ++i) {
for (int i = 0, iter_limit = params_with_default.size(); i < iter_limit; ++i) {
attrs_.push_back(params_with_default[i].GetName());
}