tensorflow · tensorflow-copybara · Jun 17, 2020 · Jun 11, 2020 · Jun 14, 2020 · Jun 14, 2020
diff --git a/tensorflow/compiler/mlir/lite/quantization/import_quant_stats_pass.cc b/tensorflow/compiler/mlir/lite/quantization/import_quant_stats_pass.cc
@@ -76,7 +76,7 @@ class ImportQuantStatsPass
   // If the index is out of range, this method returns false. Otherwise it
   // returns true if the value is a float tensor.
   bool IsQuantizableResult(Operation *op, int index) {
-    if (index < 0 || index >= op->getNumResults()) return false;
+    if (index < 0 || index >= static_cast<int>(op->getNumResults())) return false;
     Value res = op->getResult(index);
     return res.getType().isa<ShapedType>() &&
            res.getType().cast<ShapedType>().getElementType().isa<FloatType>();
@@ -158,7 +158,7 @@ void ImportQuantStatsPass::ImportAsStatsOps(OpBuilder b, Operation *op,
     InsertStatsOpAtResult(b, op->getResult(index), layer_stats, axis_stats,
                           axis);
   } else {
-    for (int i = 0; i < op->getNumResults(); ++i) {
+    for (int i = 0, e = op->getNumResults(); i < e; ++i) {
       if (IsQuantizableResult(op, i)) {
         InsertStatsOpAtResult(b, op->getResult(i), layer_stats, axis_stats,
                               axis);

diff --git a/tensorflow/compiler/mlir/lite/quantization/quantization_config.cc b/tensorflow/compiler/mlir/lite/quantization/quantization_config.cc
@@ -48,7 +48,7 @@ bool ParseInputNodeQuantSpecs(absl::string_view node_names,
   std::vector<double> node_mins;
   if (!min_values.empty()) {
     std::vector<std::string> node_mins_str = absl::StrSplit(min_values, ',');
-    for (int i = 0; i < node_mins_str.size(); i++) {
+    for (int i : llvm::seq<int>(node_mins_str.size())) {
       double value;
       if (!absl::SimpleAtod(node_mins_str[i], &value)) {
         return true;
@@ -60,7 +60,7 @@ bool ParseInputNodeQuantSpecs(absl::string_view node_names,
   std::vector<double> node_maxs;
   if (!max_values.empty()) {
     std::vector<std::string> node_maxs_str = absl::StrSplit(max_values, ',');
-    for (int i = 0; i < node_maxs_str.size(); i++) {
+    for (int i : llvm::seq<int>(node_maxs_str.size())) {
       double value;
       if (!absl::SimpleAtod(node_maxs_str[i], &value)) {
         llvm::errs() << "Unexpected mins: " << node_maxs_str[i] << "\n";

diff --git a/tensorflow/compiler/mlir/lite/quantization/quantization_driver.cc b/tensorflow/compiler/mlir/lite/quantization/quantization_driver.cc
@@ -294,7 +294,7 @@ class QuantizationDriver {
         return;
       if (current_op == op) llvm::errs() << "===>>>";
       llvm::errs() << op->getName() << " : (";
-      for (auto i = 0; i < op->getNumOperands(); ++i) {
+      for (int i = 0, e = op->getNumOperands(); i < e; ++i) {
         if (auto params = GetOperandQuantState(op, i).params)
           params.print(llvm::errs());
         else
@@ -303,7 +303,7 @@ class QuantizationDriver {
         llvm::errs() << ",";
       }
       llvm::errs() << ") -> (";
-      for (auto i = 0; i < op->getNumResults(); ++i) {
+      for (int i = 0, e = op->getNumResults(); i < e; ++i) {
         if (auto params = GetResultQuantState(op, i).params)
           params.print(llvm::errs());
         else

diff --git a/tensorflow/compiler/mlir/lite/quantization/quantization_utils.cc b/tensorflow/compiler/mlir/lite/quantization/quantization_utils.cc
@@ -54,7 +54,7 @@ static Type GetQuantizedType(Builder builder, Type input_type,
   } else if (min.size() == max.size()) {
     auto shape = input_type.dyn_cast<ShapedType>();
     if (!shape || shape.getRank() <= quant_dim ||
-        min.size() != shape.getDimSize(quant_dim)) {
+        static_cast<int64_t>(min.size()) != shape.getDimSize(quant_dim)) {
       return {};
     }
     // TODO(b/141508873): the quantization dim is set to the last dimension.
@@ -75,7 +75,7 @@ TypeAttr RescaleQuantizedType(Type input, Attribute factor) {
   if (auto qtype = ele_type.dyn_cast<quant::UniformQuantizedPerAxisType>()) {
     ArrayRef<double> scales = qtype.getScales();
     // Broadcasting hasn't been implemented yet.
-    if (scales.size() != factor_values.getNumElements()) return {};
+    if (static_cast<int64_t>(scales.size()) != factor_values.getNumElements()) return {};
     SmallVector<double, 4> new_scales;
     new_scales.reserve(scales.size());
     auto scales_iter = scales.begin();
@@ -269,7 +269,7 @@ Type GetUniformQuantizedPerAxisTypeForWeight(ElementsAttr attr, int quant_dim,
                                              bool narrow_range) {
   Builder builder(attr.getContext());
   auto shape = attr.getType().cast<ShapedType>().getShape();
-  if (shape.size() <= quant_dim) return {};
+  if (static_cast<int>(shape.size()) <= quant_dim) return {};
   // `symmetric` can only be used when it is `signed` and `narrow_range`.
   if (symmetric && (!is_signed || !narrow_range)) return {};
 
@@ -334,7 +334,7 @@ quant::QuantizedType GetUniformQuantizedTypeForBias(
     const std::vector<quant::QuantizedType>& op_types) {
   if (op_types.empty()) return {};
 
-  int axis_size = 1;
+  size_t axis_size = 1;
   int32_t quant_dim = -1;
   Type expressed_type;
   // Requires all the op types are valid UniformQuantizedTypes or
@@ -368,7 +368,7 @@ quant::QuantizedType GetUniformQuantizedTypeForBias(
         scales[index_scale.index()] *= index_scale.value();
       }
     } else if (auto type = op_type.dyn_cast<quant::UniformQuantizedType>()) {
-      for (int index = 0; index != axis_size; ++index) {
+      for (int index = 0, e = axis_size; index != e; ++index) {
         scales[index] *= type.getScale();
       }
     }

diff --git a/tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc b/tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc
@@ -41,7 +41,7 @@ std::string MakeUniqueFilename(string name) {
   static NameCounts& instance = *new NameCounts;
 
   // Remove illegal characters from `name`.
-  for (int i = 0; i < name.size(); ++i) {
+  for (int i = 0, e = name.size(); i < e; ++i) {
     char ch = name[i];
     if (ch == '/' || ch == '[' || ch == ']' || ch == '*' || ch == '?' ||
         ch == '\\') {

diff --git a/tensorflow/compiler/mlir/xla/ir/chlo_ops.cc b/tensorflow/compiler/mlir/xla/ir/chlo_ops.cc
@@ -49,7 +49,7 @@ static Type GetBroadcastType(Type x, Type y, Type element_type,
 
   if (shape_x.size() == shape_y.size()) {
     llvm::SmallVector<int64_t, 4> out_shape(shape_x.size());
-    for (int i = 0; i < shape_x.size(); i++) {
+    for (int i = 0, e = shape_x.size(); i < e; i++) {
       auto x_val = shape_x[i];
       auto y_val = shape_y[i];
       if (x_val == -1 || y_val == -1) {

diff --git a/tensorflow/compiler/mlir/xla/ir/hlo_ops.cc b/tensorflow/compiler/mlir/xla/ir/hlo_ops.cc
@@ -143,7 +143,7 @@ DenseIntElementsAttr BuildConvPaddingAttrs(
 
   int rank = padding_low.size();
   SmallVector<int64_t, 8> padding;
-  for (unsigned i = 0; i < rank; ++i) {
+  for (unsigned i = 0, e = rank; i < e; ++i) {
     padding.push_back(GetPaddingValue(padding_attr, {i, 0}) + padding_low[i]);
     padding.push_back(GetPaddingValue(padding_attr, {i, 1}) + padding_high[i]);
   }
@@ -853,7 +853,7 @@ static Attribute foldConcatenateHelper(ConcatenateOp* op,
   auto shape = type.getShape();
 
   size_t top_size = 1;
-  for (int i = 0; i < axis; i++) {
+  for (int i = 0, e = axis; i < e; i++) {
     top_size = top_size * shape[i];
   }
 
@@ -1118,7 +1118,7 @@ static LogicalResult Verify(MapOp op) {
   // increasing.
   auto values = op.dimensions().getValues<int64_t>();
   auto dimensions = std::vector<int64_t>{values.begin(), values.end()};
-  for (int i = 0; i < dimensions.size(); ++i) {
+  for (int i = 0, e = dimensions.size(); i < e; ++i) {
     if (dimensions[i] != i)
       return op.emitOpError() << "requires monotonically increasing dimension "
                                  "numbers, but got: "

diff --git a/tensorflow/compiler/xla/window_util.cc b/tensorflow/compiler/xla/window_util.cc
@@ -42,7 +42,7 @@ Window MakeWindow(absl::Span<const int64> sizes,
                   absl::Span<const int64> strides) {
   Window window;
   CHECK_EQ(sizes.size(), strides.size());
-  for (auto nb = 0; nb < sizes.size(); ++nb) {
+  for (auto nb = 0; static_cast<size_t>(nb) < sizes.size(); ++nb) {
     auto* dimension = window.add_dimensions();
     dimension->set_size(sizes[nb]);
     dimension->set_stride(strides[nb]);

diff --git a/tensorflow/core/kernels/batch_kernels.cc b/tensorflow/core/kernels/batch_kernels.cc
@@ -486,18 +486,18 @@ class BatchResource : public ResourceBase {
     std::map<string, std::vector<Tensor>> split_tensors;
 
     DCHECK_EQ(batch->task(0).context->num_outputs(), combined_outputs.size());
-    if (combined_outputs.size() != batch->task(0).context->num_outputs()) {
+    if (static_cast<int>(combined_outputs.size()) != batch->task(0).context->num_outputs()) {
       return errors::Internal("Wrong number of batched output tensors");
     }
 
     // Generate 'split_tensors' and populate the context outputs.
-    for (int i = 0; i < combined_outputs.size(); ++i) {
+    for (size_t i = 0; i < combined_outputs.size(); ++i) {
       const Tensor& output_tensor = combined_outputs[i];
       if (output_tensor.shape().dims() == 0) {
         return errors::FailedPrecondition(
             "Batched output tensor has 0 dimensions");
       }
-      if (output_tensor.shape().dim_size(0) != batch->size() + padding_size) {
+      if (output_tensor.shape().dim_size(0) != static_cast<long long int>(batch->size() + padding_size)) {
         return errors::FailedPrecondition(
             "Batched output tensor's 0th dimension does not equal the sum of "
             "the 0th dimension sizes of the input tensors");

diff --git a/tensorflow/core/kernels/data/prefetch_autotuner.cc b/tensorflow/core/kernels/data/prefetch_autotuner.cc
@@ -40,13 +40,13 @@ void PrefetchAutotuner::RecordConsumption(size_t current_buffer_size) {
     case Mode::kDisabled:
       return;
     case Mode::kUpswing:
-      if (current_buffer_size == buffer_limit_) {
+      if (static_cast<tensorflow::int64>(current_buffer_size) == buffer_limit_) {
         mode_ = Mode::kDownswing;
       }
       return;
     case Mode::kDownswing:
       if (current_buffer_size == 0) {
-        if (buffer_limit_ >= kBufferLimitThreshold) {
+        if (buffer_limit_ >= static_cast<tensorflow::int64>(kBufferLimitThreshold)) {
           buffer_limit_ += kBufferLimitThreshold;
         } else {
           buffer_limit_ *= 2;

diff --git a/tensorflow/core/kernels/quantization_utils.h b/tensorflow/core/kernels/quantization_utils.h
@@ -268,7 +268,7 @@ inline void RequantizeManyInNewRangeReference(const qint32* input, int64 count,
   // that could be easily adapted for a SIMD implementation. It should also be
   // possible to perform all the calculations in 32-bit rather than 64, but
   // that's not been implemented yet.
-  for (size_t index = 0; index < count; ++index) {
+  for (size_t index = 0; static_cast<tensorflow::int64>(index) < count; ++index) {
     const int64 input_value = static_cast<int64>(input[index]);
     const int64 fp_value =
         ((input_value * range_scale_fp) >> 32) + input_offset_fp;

diff --git a/tensorflow/core/platform/s3/s3_file_system.cc b/tensorflow/core/platform/s3/s3_file_system.cc
@@ -906,7 +906,7 @@ Status S3FileSystem::MultiPartCopy(const Aws::String& source,
       // wait on the mutex until notify is called
       // then check the finished parts as there could be false notifications
       multi_part_copy_cv.wait(lock, [&finishedPartStates, num_parts] {
-        return finishedPartStates.size() == num_parts;
+        return static_cast<const int>(finishedPartStates.size()) == num_parts;
       });
     }
     // check if there was any error for any part

diff --git a/tensorflow/core/profiler/utils/derived_timeline.cc b/tensorflow/core/profiler/utils/derived_timeline.cc
@@ -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; i < static_cast<int>(last_event_by_level_.size()); ++i) {
     last_event_by_level_[i] = absl::nullopt;
   }
   if (level == 0) ResetDependentLines();

diff --git a/tensorflow/core/profiler/utils/derived_timeline.h b/tensorflow/core/profiler/utils/derived_timeline.h
@@ -37,7 +37,7 @@ class DerivedXLineBuilder {
                       std::vector<DerivedXLineBuilder*> dependent_lines);
 
   void ExpandOrAddEvents(const std::vector<XEvent>& event_per_level) {
-    for (int level = 0; level < event_per_level.size(); ++level) {
+    for (size_t level = 0; level < event_per_level.size(); ++level) {
       ExpandOrAddLevelEvent(event_per_level[level], level);
     }
   }

diff --git a/tensorflow/core/profiler/utils/xplane_utils.cc b/tensorflow/core/profiler/utils/xplane_utils.cc
@@ -266,7 +266,7 @@ 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<long int>(start_time_ns)) {
       line.set_timestamp_ns(line.timestamp_ns() - start_time_ns);
     }
   }

diff --git a/tensorflow/core/util/bcast.h b/tensorflow/core/util/bcast.h
@@ -139,7 +139,7 @@ BCastList<N>::BCastList(const BCastList::Vec (&x)[N],
     if (x[i] != x[0]) {
       all_equal = false;
     }
-    if (x[i].size() > largest_rank) {
+    if (static_cast<int>(x[i].size()) > largest_rank) {
       largest_rank = x[i].size();
     }
   }
@@ -176,7 +176,7 @@ 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) {
+    if (static_cast<int>(copy[i].size()) < largest_rank) {
       copy[i].resize(largest_rank, 1);
     }
   }

diff --git a/tensorflow/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc b/tensorflow/lite/toco/graph_transformations/convert_trivial_tile_to_concat.cc
@@ -52,7 +52,7 @@ ::tensorflow::Status ConvertTrivialTileToConcat::Run(Model* model,
   // It then just becomes a concat along that dimension.
   int non_one_dims = 0;
   int concat_axis = 0;
-  for (int i = 0; i < multiples.size(); ++i) {
+  for (size_t i = 0; i < multiples.size(); ++i) {
     if (multiples[i] != 1) {
       ++non_one_dims;
       concat_axis = i;

diff --git a/tensorflow/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc b/tensorflow/lite/toco/graph_transformations/convert_trivial_transpose_to_reshape.cc
@@ -31,7 +31,7 @@ bool TransposeAffectsMemoryOrder(std::vector<int> perm,
   // just the shape) then the flat buffer representation shouldn't change.
   std::vector<int> old_major_index_ordering;
   std::vector<int> new_major_index_ordering;
-  for (int i = 0; i < in_shape.size(); i++) {
+  for (int i = 0; static_cast<size_t>(i) < in_shape.size(); i++) {
     if (in_shape[i] != 1) {
       old_major_index_ordering.push_back(i);
     }

diff --git a/tensorflow/lite/toco/graph_transformations/dequantize.cc b/tensorflow/lite/toco/graph_transformations/dequantize.cc
@@ -35,7 +35,7 @@ void DequantizeBuffer(Array* array) {
   auto& new_data = array->GetMutableBuffer<ArrayDataType::kFloat>().data;
   new_data.resize(old_data.size());
   const auto& qparams = array->GetQuantizationParams();
-  for (int i = 0; i < old_data.size(); i++) {
+  for (size_t i = 0; i < old_data.size(); i++) {
     new_data[i] = qparams.scale * (old_data[i] - qparams.zero_point);
   }
 }

diff --git a/tensorflow/lite/toco/graph_transformations/drop_fake_quant.cc b/tensorflow/lite/toco/graph_transformations/drop_fake_quant.cc
@@ -45,7 +45,7 @@ ::tensorflow::Status DropFakeQuant::Run(Model* model, std::size_t op_index,
   }
 
   // Drop min/max inputs
-  for (int i = 1; i < fakequant_op->inputs.size(); i++) {
+  for (size_t i = 1; i < fakequant_op->inputs.size(); i++) {
     if (CountOpsWithInput(*model, fakequant_op->inputs[i]) == 1) {
       model->EraseArray(fakequant_op->inputs[i]);
     }

diff --git a/...orflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc b/...orflow/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc
@@ -166,7 +166,7 @@ ::tensorflow::Status EnsureUint8WeightsSafeForFastInt8Kernels::Run(
   int index_of_previous_bad_value = 0;
   bool changed = false;
 
-  for (int i = 0; i < buffer_data.size(); i++) {
+  for (size_t i = 0; i < buffer_data.size(); i++) {
     if (buffer_data[i] == 0) {
       count_bad++;
       if (count_bad > 1) {

diff --git a/tensorflow/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc b/tensorflow/lite/toco/graph_transformations/fuse_broadcast_into_following_binary.cc
@@ -34,7 +34,7 @@ bool IsBroadcastingOp(const Model& model, Operator* op) {
   // Concatenation of identical inputs is usually a broadcast.
   if (op->type == OperatorType::kConcatenation) {
     // Verify that all inputs are the same.
-    for (int i = 1; i < op->inputs.size(); ++i) {
+    for (size_t i = 1; i < op->inputs.size(); ++i) {
       if (op->inputs[i] != op->inputs[0]) {
         return false;
       }

diff --git a/tensorflow/lite/toco/graph_transformations/group_bidirectional_sequence_ops.cc b/tensorflow/lite/toco/graph_transformations/group_bidirectional_sequence_ops.cc
@@ -125,7 +125,7 @@ bool CheckTwoUnidirectionalSequenceOpsAreValid(
       return false;
 
     // Make sure the inputs datatype matches.
-    for (int i = 0; i < fw_sequence_op->inputs.size(); ++i) {
+    for (size_t i = 0; i < fw_sequence_op->inputs.size(); ++i) {
       const auto& fw_input_array_name = fw_sequence_op->inputs[i];
       const auto& bw_input_array_name = bw_sequence_op->inputs[i];
       if (model.HasArray(fw_input_array_name) &&
@@ -137,7 +137,7 @@ bool CheckTwoUnidirectionalSequenceOpsAreValid(
     }
 
     // Make sure the outputs datatype matches.
-    for (int i = 0; i < fw_sequence_op->outputs.size(); ++i) {
+    for (size_t i = 0; i < fw_sequence_op->outputs.size(); ++i) {
       const auto& fw_output_array_name = fw_sequence_op->outputs[i];
       const auto& bw_output_array_name = bw_sequence_op->outputs[i];
       if (model.HasArray(fw_output_array_name) &&

diff --git a/tensorflow/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/lite/toco/graph_transformations/hardcode_min_max.cc
@@ -405,7 +405,7 @@ bool HardcodeMinMaxForPack(Model* model, Operator* op) {
   }
   const auto& first_input_minmax = first_input_array.GetMinMax();
 
-  for (int i = 1; i < op->inputs.size(); i++) {
+  for (size_t i = 1; i < op->inputs.size(); i++) {
     const auto& input_array = model->GetArray(op->inputs[i]);
     if (!input_array.minmax) {
       return false;

diff --git a/tensorflow/lite/toco/graph_transformations/identify_nearest_upsample.cc b/tensorflow/lite/toco/graph_transformations/identify_nearest_upsample.cc
@@ -199,7 +199,7 @@ ::tensorflow::Status IdentifyNearestUpsample::Run(Model* model,
   shape_array.data_type = ArrayDataType::kInt32;
   auto& shape_buffer = shape_array.GetMutableBuffer<ArrayDataType::kInt32>();
   // This is what imagined as the original shape.
-  for (int i = 0; i < imagined_original_shape.size(); ++i) {
+  for (size_t i = 0; i < imagined_original_shape.size(); ++i) {
     shape_buffer.data.push_back(imagined_original_shape.at(i));
   }
 

diff --git a/tensorflow/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc b/tensorflow/lite/toco/graph_transformations/merge_reshape_into_preceding_transpose.cc
@@ -70,7 +70,7 @@ std::vector<int32> ReshapeToTranspose(const Model& model,
   std::vector<int> not_one_indices;
 
   // Separate into one indices and not one indices.
-  for (int i = 0; i < in_shape.size(); i++) {
+  for (size_t i = 0; i < in_shape.size(); i++) {
     if (in_shape[i] == 1) {
       one_indices.push_back(i);
     } else {
@@ -167,7 +167,7 @@ ::tensorflow::Status MergeReshapeIntoPrecedingTranspose::Run(
 
   // Combine the permutations.
   const auto& transpose_perm = transpose_op->perm;
-  for (int i = 0; i < merged_perm.size(); i++) {
+  for (size_t i = 0; i < merged_perm.size(); i++) {
     merged_perm[i] = transpose_perm[merged_perm[i]];
   }
 

diff --git a/tensorflow/lite/toco/graph_transformations/propagate_array_data_types.cc b/tensorflow/lite/toco/graph_transformations/propagate_array_data_types.cc
@@ -170,7 +170,7 @@ ::tensorflow::Status PropagateArrayDataTypes::Run(Model* model,
       if (unsupported_op->output_data_types.size() < op->outputs.size()) {
         return ::tensorflow::Status::OK();
       }
-      for (int i = 0; i < op->outputs.size(); ++i) {
+      for (size_t i = 0; i < op->outputs.size(); ++i) {
         const string& output = op->outputs[i];
         const ArrayDataType data_type = unsupported_op->output_data_types[i];
         model->GetArray(output).data_type = data_type;

diff --git a/tensorflow/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc b/tensorflow/lite/toco/graph_transformations/propagate_fake_quant_num_bits.cc
@@ -149,7 +149,7 @@ bool RecursivelyBackwardPropagateDataType(GraphTransformation* transformation,
                                           ArrayDataType new_data_type,
                                           const MinMax& new_minmax) {
   bool did_change = false;
-  for (int input_index = 0; input_index < op->inputs.size(); ++input_index) {
+  for (size_t input_index = 0; input_index < op->inputs.size(); ++input_index) {
     const auto& input = op->inputs[input_index];
     auto& input_array = model->GetArray(input);