comment out unused parameters

Summary: This uses `clang-tidy` to comment out unused parameters (in functions, methods and lambdas) in fbcode. Cases that the tool failed to handle are fixed manually.

Reviewed By: igorsugak

Differential Revision: D5454343

fbshipit-source-id: 5dee339b4334e25e963891b519a5aa81fbf627b2

Author: vgao1996

caffe2/binaries/make_image_db.cc

@@ -37,8 +37,10 @@ CAFFE2_DEFINE_bool(warp, false, "If warp is set, warp the images to square.");
 namespace caffe2 {
 
 void ConvertImageDataset(
-    const string& input_folder, const string& list_filename,
-    const string& output_db_name, const bool shuffle) {
+    const string& input_folder,
+    const string& list_filename,
+    const string& output_db_name,
+    const bool /*shuffle*/) {
   std::ifstream list_file(list_filename);
   std::vector<std::pair<std::string, int> > lines;
   std::string filename;

caffe2/contrib/nervana/nervana_math_gpu.cc

@@ -22,7 +22,7 @@ void Gemm<float, CUDAContext, NervanaEngine>(
     const float beta,
     float* C,
     CUDAContext* context,
-    TensorProto::DataType math_type) {
+    TensorProto::DataType /*math_type*/) {
   // Note that cublas follows fortran order, so the order is different from
   // the cblas convention.
   int lda = (TransA == CblasNoTrans) ? K : M;

caffe2/contrib/prof/cuda_profile_ops.cc

@@ -56,7 +56,7 @@ class CudaProfileInitializeOp : public OperatorBase {
     unlink(config_.c_str());
   }
 
-  virtual bool Run(int /* unused */ stream_id = 0) {
+  virtual bool Run(int /* unused */ /*stream_id*/ = 0) {
     // If this fails, check the contents of "output" for hints.
     CUDA_CHECK(
         cudaProfilerInitialize(config_.c_str(), output_.c_str(), cudaCSV));
@@ -73,7 +73,7 @@ class CudaProfileStartOp : public OperatorBase {
   CudaProfileStartOp(const OperatorDef& operator_def, Workspace* ws)
       : OperatorBase(operator_def, ws) {}
 
-  virtual bool Run(int /* unused */ stream_id = 0) {
+  virtual bool Run(int /* unused */ /*stream_id*/ = 0) {
     CUDA_ENFORCE(cudaProfilerStart());
     return true;
   }
@@ -84,7 +84,7 @@ class CudaProfileStopOp : public OperatorBase {
   CudaProfileStopOp(const OperatorDef& operator_def, Workspace* ws)
       : OperatorBase(operator_def, ws) {}
 
-  virtual bool Run(int /* unused */ stream_id = 0) {
+  virtual bool Run(int /* unused */ /*stream_id*/ = 0) {
     CUDA_ENFORCE(cudaProfilerStop());
     return true;
   }

caffe2/contrib/torch/torch_op.h

@@ -56,7 +56,7 @@ class Torch final {
     return Traits::tensorTy;
   }
 
-  void setContext(Context* context) {}
+  void setContext(Context* /*context*/) {}
 
   void setTensor(typename Traits::Tensor* t, Blob* blob) {
     CAFFE_ENFORCE_EQ(tensorTy(*blob), Traits::tensorTy);

caffe2/contrib/transform/transform.h

@@ -63,9 +63,9 @@ class Transform {
    * Given the current subgraph (ordered), should we append the new node at idx?
    */
   virtual bool PatternRule(
-      const transform::Graph& g,
-      const std::vector<int>& subgraph,
-      int idx) {
+      const transform::Graph& /*g*/,
+      const std::vector<int>& /*subgraph*/,
+      int /*idx*/) {
     CAFFE_NOT_IMPLEMENTED;
   }
 
@@ -74,8 +74,8 @@ class Transform {
    * Given a subgraph, can we accept it?
    */
   virtual bool ValidatorRule(
-      const transform::Graph& g,
-      const std::vector<int>& subgraph) {
+      const transform::Graph& /*g*/,
+      const std::vector<int>& /*subgraph*/) {
     CAFFE_NOT_IMPLEMENTED;
   }
 
@@ -84,8 +84,8 @@ class Transform {
    * upon the subgraph.
    */
   virtual bool ReplaceRule(
-      const std::vector<int>& subgraph,
-      transform::Graph* g_ptr) {
+      const std::vector<int>& /*subgraph*/,
+      transform::Graph* /*g_ptr*/) {
     CAFFE_NOT_IMPLEMENTED;
   }
 

caffe2/contrib/warpctc/ctc_op.cpp

@@ -6,7 +6,7 @@ namespace caffe2 {
 
 namespace detail {
 template <>
-ctcComputeInfo workspaceInfo<CPUContext>(const CPUContext& context) {
+ctcComputeInfo workspaceInfo<CPUContext>(const CPUContext& /*context*/) {
   ctcComputeInfo result;
   result.loc = CTC_CPU;
   result.num_threads = 1;

caffe2/core/blob_serialization.cc

@@ -86,7 +86,8 @@ std::string Blob::Serialize(const string& name) const {
 // Specialization for StoreDeviceDetail for CPU - nothing needs to be done.
 template <>
 void TensorSerializer<CPUContext>::StoreDeviceDetail(
-    const Tensor<CPUContext>& input, TensorProto* proto) {}
+    const Tensor<CPUContext>& /*input*/,
+    TensorProto* /*proto*/) {}
 
 // The actual serialization registry objects.
 CAFFE_DEFINE_TYPED_REGISTRY(

caffe2/core/blob_serialization.h

@@ -263,8 +263,11 @@ void TensorSerializer<Context>::SerializeWithChunkSize(
 
 template <class Context>
 void TensorSerializer<Context>::Serialize(
-    const Tensor<Context>& input, const string& name,
-    TensorProto* proto_ptr, size_t chunkBegin, int32_t chunkSize) {
+    const Tensor<Context>& input,
+    const string& /*name*/,
+    TensorProto* proto_ptr,
+    size_t chunkBegin,
+    int32_t chunkSize) {
   CAFFE_ENFORCE(
       chunkBegin <= input.size(),
       "Chunk begin is out of tensor: ",

caffe2/core/blob_serializer_base.h

@@ -46,7 +46,7 @@ class BlobSerializerBase {
       const Blob& blob,
       const std::string& name,
       SerializationAcceptor acceptor,
-      int chunk_size) {
+      int /*chunk_size*/) {
     // Base implementation.
     Serialize(blob, name, acceptor);
   }

caffe2/core/blob_test.cc

@@ -649,7 +649,7 @@ class VectorCursor : public db::Cursor {
     pos_ = 0;
   }
   ~VectorCursor() {}
-  void Seek(const string& key) override {}
+  void Seek(const string& /*key*/) override {}
   void SeekToFirst() override {}
   void Next() override {
     ++pos_;
@@ -790,7 +790,8 @@ TEST(CustomChunkSize, BigTensorSerialization) {
   tensor->mutable_data<float>();
   std::mutex mutex;
   int counter = 0;
-  auto acceptor = [&](const std::string& key, const std::string& value) {
+  auto acceptor = [&](const std::string& /*key*/,
+                      const std::string& /*value*/) {
     std::lock_guard<std::mutex> guard(mutex);
     counter++;
   };