fix some compiler warning

test=develop

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc

@@ -412,7 +412,7 @@ void DetachDeletedNodes(framework::ir::Graph *graph) {
 void SubGraphFuser::ReplaceNodesWithSubGraphs() {
   auto subgraphs = SubgraphDetector(graph_, node_inside_subgraph_teller_)();
   for (auto &subgraph : subgraphs) {
-    if (subgraph.size() <= min_subgraph_size_) continue;
+    if (subgraph.size() <= (size_t)min_subgraph_size_) continue;
     LOG(INFO) << "detect a subgraph size " << subgraph.size();
     std::unordered_set<Node *> subgraph_uniq(subgraph.begin(), subgraph.end());
     // replace this sub-graph with the first node. Two steps: 1. Create a Block

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -114,7 +114,7 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
   // it is either an OP's input or an OP's output.
 
   auto &subgraph_nodes = *Agent(node).subgraph();
-  for (int index = 0; index < block_desc.OpSize(); index++) {
+  for (size_t index = 0; index < block_desc.OpSize(); index++) {
     framework::proto::OpDesc *op = block_desc.Op(index)->Proto();
     auto correspond_node = subgraph_nodes[index];
     PADDLE_ENFORCE_EQ(correspond_node->Name(), op->type());

paddle/fluid/inference/tests/api/analyzer_dam_tester.cc

@@ -69,7 +69,7 @@ struct DataRecord {
       num_lines++;
       std::vector<std::string> data;
       split(line, ',', &data);
-      CHECK_EQ(data.size(), 2 * MAX_TURN_NUM + 3);
+      CHECK_EQ(data.size(), (size_t)(2 * MAX_TURN_NUM + 3));
       // load turn data
       std::vector<int64_t> turns_tmp[MAX_TURN_NUM];
       for (int i = 0; i < MAX_TURN_NUM; ++i) {

paddle/fluid/operators/hash_op.cc

@@ -38,7 +38,7 @@ class HashOp : public framework::OperatorWithKernel {
     std::vector<int64_t> out_dims;
     out_dims.reserve(dims.size() + 1);
     // copy all dims except the last one
-    for (size_t i = 0u; i != dims.size() - 1; ++i) {
+    for (int i = 0u; i != dims.size() - 1; ++i) {
       out_dims.emplace_back(dims[i]);
     }
     int num_hash = ctx->Attrs().Get<int>("num_hash");

paddle/fluid/operators/math/selected_rows_functor.cc

@@ -244,7 +244,7 @@ typename std::enable_if<
     std::is_same<DeviceContext, platform::CPUDeviceContext>::value>::type
 elementwise_add_to(const DeviceContext& ctx, BlasT<DeviceContext, T>* blas,
                    size_t data_len, const T* in, T* out) {
-  for (int64_t i = 0; i < data_len; i++) {
+  for (size_t i = 0; i < data_len; i++) {
     out[i] += in[i];
   }
 }

paddle/fluid/operators/math/sequence_pooling_test.cc

@@ -70,11 +70,11 @@ void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
   EXPECT_EQ(in_grad.lod(), lod);
 
   if (paddle::platform::is_cpu_place(*place)) {
-    for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
+    for (size_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
       int64_t begin = in_grad.lod()[0][i];
       int64_t end = in_grad.lod()[0][i + 1];
       paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
-      for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
+      for (size_t j = 0; j != tmp.numel() / second_dim; ++j) {
         for (int64_t m = 0; m != second_dim; ++m) {
           EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
                     out_grad.data<T>()[m + i * second_dim]);
@@ -82,11 +82,11 @@ void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
       }
     }
   } else {
-    for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
+    for (size_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
       int64_t begin = cpu_in_grad.lod()[0][i];
       int64_t end = cpu_in_grad.lod()[0][i + 1];
       paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
-      for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
+      for (size_t j = 0; j != tmp.numel() / second_dim; ++j) {
         for (int64_t m = 0; m != second_dim; ++m) {
           EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
                     cpu_out_grad.data<T>()[m + i * second_dim]);

paddle/fluid/operators/merge_ids_op.h

@@ -43,11 +43,11 @@ class MergeIdsOpKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_EQ(ids.size(), outs.size(),
                       "the number of Ids and Out should be the same");
 
-    int row_ids_size = 0;
+    size_t row_ids_size = 0;
     int row_size = 0;
     int embedding_size = 0;
 
-    for (int i = 0; i < x_tensors.size(); ++i) {
+    for (size_t i = 0; i < x_tensors.size(); ++i) {
       const auto *x_tensor = x_tensors[i];
       const auto *row_id = row_ids[i];
 
@@ -66,7 +66,7 @@ class MergeIdsOpKernel : public framework::OpKernel<T> {
 
     std::unordered_map<int64_t, std::tuple<int64_t, int64_t>>
         selected_rows_idx_map;
-    for (int i = 0; i < x_tensors.size(); ++i) {
+    for (size_t i = 0; i < x_tensors.size(); ++i) {
       const auto *row_id = row_ids[i];
 
       for (int j = 0; j < row_id->numel(); ++j) {
@@ -78,7 +78,7 @@ class MergeIdsOpKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_EQ(row_ids_size, selected_rows_idx_map.size(),
                       "the rows and tensor map size should be the same");
 
-    for (int i = 0; i < outs.size(); ++i) {
+    for (size_t i = 0; i < outs.size(); ++i) {
       auto *out_ids = ids[i];
       auto *out = outs[i];
 

paddle/fluid/operators/ref_by_trainer_id_op.h

@@ -38,7 +38,7 @@ class RefByTrainerIdKernel : public framework::OpKernel<T> {
     } else {
       trainer_id = *trainer_id_data;
     }
-    PADDLE_ENFORCE_LT(trainer_id, in_list.size());
+    PADDLE_ENFORCE_LT((size_t)trainer_id, in_list.size());
     out->mutable_data<T>(context.GetPlace());
     out->ShareDataWith(*(in_list[trainer_id]));
   }

paddle/fluid/operators/split_ids_op.h

@@ -64,7 +64,7 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
       out_ids.resize(outs.size());
 
       // split id by their shard_num.
-      for (int i = 0; i < all_ids.size(); ++i) {
+      for (size_t i = 0; i < all_ids.size(); ++i) {
         T id = all_ids[i];
         size_t shard_id = static_cast<size_t>(id) % shard_num;
         out_ids[shard_id].push_back(id);