Merge pull request #16090 from lidanqing-intel/paddle-int32

Add PaddleDType INT32 support

paddle/fluid/inference/api/analysis_predictor.cc

@@ -243,6 +243,8 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       input_ptr = input.mutable_data<int64_t>(ddim, place_);
     } else if (inputs[i].dtype == PaddleDType::FLOAT32) {
       input_ptr = input.mutable_data<float>(ddim, place_);
+    } else if (inputs[i].dtype == PaddleDType::INT32) {
+      input_ptr = input.mutable_data<int32_t>(ddim, place_);
     } else {
       LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
       return false;
@@ -326,8 +328,11 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
     } else if (type == framework::proto::VarType::INT64) {
       GetFetchOne<int64_t>(fetch, output);
       output->dtype = PaddleDType::INT64;
+    } else if (type == framework::proto::VarType::INT32) {
+      GetFetchOne<int32_t>(fetch, output);
+      output->dtype = PaddleDType::INT32;
     } else {
-      LOG(ERROR) << "unknown type, only support float32 and int64 now.";
+      LOG(ERROR) << "unknown type, only support float32, int64 and int32 now.";
     }
   }
   return true;

paddle/fluid/inference/api/api.cc

@@ -28,6 +28,8 @@ int PaddleDtypeSize(PaddleDType dtype) {
       return sizeof(float);
     case PaddleDType::INT64:
       return sizeof(int64_t);
+    case PaddleDType::INT32:
+      return sizeof(int32_t);
     default:
       assert(false);
       return -1;

paddle/fluid/inference/api/api_impl.cc

@@ -203,6 +203,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
       input_ptr = input.mutable_data<int64_t>(ddim, place_);
     } else if (inputs[i].dtype == PaddleDType::FLOAT32) {
       input_ptr = input.mutable_data<float>(ddim, place_);
+    } else if (inputs[i].dtype == PaddleDType::INT32) {
+      input_ptr = input.mutable_data<int32_t>(ddim, place_);
     } else {
       LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
       return false;
@@ -281,8 +283,11 @@ bool NativePaddlePredictor::GetFetch(std::vector<PaddleTensor> *outputs,
     } else if (type == framework::DataTypeTrait<int64_t>::DataType) {
       GetFetchOne<int64_t>(fetch, output);
       output->dtype = PaddleDType::INT64;
+    } else if (type == framework::DataTypeTrait<int32_t>::DataType) {
+      GetFetchOne<int32_t>(fetch, output);
+      output->dtype = PaddleDType::INT32;
     } else {
-      LOG(ERROR) << "unknown type, only support float32 and int64 now.";
+      LOG(ERROR) << "unknown type, only support float32, int64 and int32 now.";
     }
   }
   return true;

paddle/fluid/inference/api/api_impl_tester.cc

@@ -42,6 +42,9 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
   } else if (t->type() == framework::proto::VarType::FP32) {
     pt.data.Reset(t->data<void>(), t->numel() * sizeof(float));
     pt.dtype = PaddleDType::FLOAT32;
+  } else if (t->type() == framework::proto::VarType::INT32) {
+    pt.data.Reset(t->data<void>(), t->numel() * sizeof(int32_t));
+    pt.dtype = PaddleDType::INT32;
   } else {
     LOG(FATAL) << "unsupported type.";
   }

paddle/fluid/inference/api/demo_ci/utils.h

@@ -88,13 +88,20 @@ void CheckOutput(const std::string& referfile, const PaddleTensor& output) {
       }
       break;
     }
-    case PaddleDType::FLOAT32:
+    case PaddleDType::FLOAT32: {
       for (size_t i = 0; i < numel; ++i) {
         CHECK_LT(
             fabs(static_cast<float*>(output.data.data())[i] - refer.data[i]),
             1e-5);
       }
       break;
+    }
+    case PaddleDType::INT32: {
+      for (size_t i = 0; i < numel; ++i) {
+        CHECK_EQ(static_cast<int32_t*>(output.data.data())[i], refer.data[i]);
+      }
+      break;
+    }
   }
 }
 
@@ -113,11 +120,18 @@ static std::string SummaryTensor(const PaddleTensor& tensor) {
       }
       break;
     }
-    case PaddleDType::FLOAT32:
+    case PaddleDType::FLOAT32: {
       for (int i = 0; i < std::min(num_elems, 10); i++) {
         ss << static_cast<float*>(tensor.data.data())[i] << " ";
       }
       break;
+    }
+    case PaddleDType::INT32: {
+      for (int i = 0; i < std::min(num_elems, 10); i++) {
+        ss << static_cast<int32_t*>(tensor.data.data())[i] << " ";
+      }
+      break;
+    }
   }
   return ss.str();
 }

paddle/fluid/inference/api/helper.h

@@ -202,6 +202,9 @@ static std::string DescribeTensor(const PaddleTensor &tensor,
     case PaddleDType::INT64:
       os << "int64";
       break;
+    case PaddleDType::INT32:
+      os << "int32";
+      break;
     default:
       os << "unset";
   }

paddle/fluid/inference/api/paddle_api.h

@@ -36,6 +36,7 @@ namespace paddle {
 enum PaddleDType {
   FLOAT32,
   INT64,
+  INT32,
   // TODO(Superjomn) support more data types if needed.
 };
 

paddle/fluid/inference/tests/api/tester_helper.h

@@ -25,7 +25,6 @@
 #ifdef WITH_GPERFTOOLS
 #include <gperftools/profiler.h>
 #endif
-
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
@@ -97,6 +96,14 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
         }
         break;
       }
+      case PaddleDType::INT32: {
+        int32_t *pdata = static_cast<int32_t *>(out.data.data());
+        int32_t *pdata_ref = static_cast<int32_t *>(ref_out.data.data());
+        for (size_t j = 0; j < size; ++j) {
+          EXPECT_EQ(pdata_ref[j], pdata[j]);
+        }
+        break;
+      }
     }
   }
 }

paddle/fluid/pybind/inference_api.cc

@@ -65,7 +65,8 @@ void BindInferenceApi(py::module *m) {
 void BindPaddleDType(py::module *m) {
   py::enum_<PaddleDType>(*m, "PaddleDType")
       .value("FLOAT32", PaddleDType::FLOAT32)
-      .value("INT64", PaddleDType::INT64);
+      .value("INT64", PaddleDType::INT64)
+      .value("INT32", PaddleDType::INT32);
 }
 
 void BindPaddleBuf(py::module *m) {
@@ -103,6 +104,11 @@ void BindPaddleBuf(py::module *m) {
              int64_t *data = static_cast<int64_t *>(self.data());
              return {data, data + self.length() / sizeof(*data)};
            })
+      .def("int32_data",
+           [](PaddleBuf &self) -> std::vector<int32_t> {
+             int32_t *data = static_cast<int32_t *>(self.data());
+             return {data, data + self.length() / sizeof(*data)};
+           })
       .def("length", &PaddleBuf::length);
 }