[JITLayer]Enable OneDNN on CPU and Fix zero shape (#47428) (#47436)

* [JITLayer]Enable OneDNN on CPU and Fix zero shape

paddle/fluid/inference/api/analysis_predictor.cc

@@ -171,8 +171,9 @@ bool PaddleTensorToLoDTensor(const PaddleTensor &pt,
   // NOTE(Aurelius84): Some kernels support zero shape input
   // without memory holder, we should skip enforce logic.
   bool has_zero_dim = (phi::product(ddim) == 0);
-  if (has_zero_dim) {
-    VLOG(3) << "Found zero dim from input with ddim: " << ddim;
+  VLOG(3) << "Found zero dim: " << has_zero_dim
+          << " from input with ddim: " << ddim;
+  if (!has_zero_dim) {
     PADDLE_ENFORCE_NOT_NULL(
         input_ptr,
         paddle::platform::errors::Fatal(

paddle/fluid/jit/engine/predictor_engine.cc

@@ -34,12 +34,16 @@ PredictorEngine::PredictorEngine(const std::shared_ptr<FunctionInfo> &info,
   utils::ShareParamsIntoScope(info_->ParamNames(), params_dict, scope_.get());
   VLOG(6) << framework::GenScopeTreeDebugInfo(scope_.get());
 
+  // TODO(Aurelius84): Expose AnalysisConfig to user.
   AnalysisConfig config;
   config.SetProgFile(info->ProgramFilePath());
   if (platform::is_gpu_place(place_)) {
     config.EnableUseGpu(100, place_.GetDeviceId());
   } else if (platform::is_cpu_place(place_)) {
     config.DisableGpu();
+    config.EnableMKLDNN();
+    config.EnableMkldnnInt8();
+    config.SetMkldnnCacheCapacity(0);
   }
   config.SetSkipLoadParams(true);
   config.SetApplyOptim(true);
@@ -59,10 +63,6 @@ std::vector<Tensor> PredictorEngine::operator()(
 
 std::vector<DenseTensor> PredictorEngine::operator()(
     const std::vector<DenseTensor> &inputs) {
-  for (auto t : inputs) {
-    VLOG(1) << "inputs is init: " << t.initialized();
-  }
-
   std::vector<PaddleTensor> pt_inputs;
   std::vector<PaddleTensor> pt_outputs;
   for (auto &t : inputs) {
@@ -84,22 +84,23 @@ std::vector<DenseTensor> PredictorEngine::operator()(
 
 static PaddleTensor DenseTensorToPaddleTensor(DenseTensor *t) {
   PaddleTensor pt;
-
-  if (framework::TransToProtoVarType(t->dtype()) ==
-      framework::proto::VarType::INT32) {
+  switch (framework::TransToProtoVarType(t->dtype())) {
+    case framework::proto::VarType::INT32: {
       pt.data.Reset(t->data(), t->numel() * sizeof(int32_t));
       pt.dtype = PaddleDType::INT32;
-  } else if (framework::TransToProtoVarType(t->dtype()) ==
-             framework::proto::VarType::INT64) {
+    } break;
+    case framework::proto::VarType::INT64: {
       pt.data.Reset(t->data(), t->numel() * sizeof(int64_t));
       pt.dtype = PaddleDType::INT64;
-  } else if (framework::TransToProtoVarType(t->dtype()) ==
-             framework::proto::VarType::FP32) {
+    } break;
+    case framework::proto::VarType::FP32: {
       pt.data.Reset(t->data(), t->numel() * sizeof(float));
       pt.dtype = PaddleDType::FLOAT32;
-  } else {
-    PADDLE_THROW(platform::errors::Unimplemented(
-        "Unsupported tensor date type. Now only supports INT64, FP32, INT32."));
+    } break;
+    default:
+      PADDLE_THROW(
+          platform::errors::Unimplemented("Unsupported tensor date type. Now "
+                                          "only supports INT64, FP32, INT32."));
   }
   pt.shape = phi::vectorize<int>(t->dims());
   return pt;
@@ -110,15 +111,20 @@ static bool PaddleTensorToDenseTensor(const PaddleTensor &pt,
                                       const platform::Place &place) {
   framework::DDim ddim = phi::make_ddim(pt.shape);
   void *input_ptr;
-  if (pt.dtype == PaddleDType::INT64) {
+  switch (pt.dtype) {
+    case PaddleDType::INT64:
       input_ptr = t->mutable_data<int64_t>(ddim, place);
-  } else if (pt.dtype == PaddleDType::FLOAT32) {
+      break;
+    case PaddleDType::FLOAT32:
       input_ptr = t->mutable_data<float>(ddim, place);
-  } else if (pt.dtype == PaddleDType::INT32) {
+      break;
+    case PaddleDType::INT32:
       input_ptr = t->mutable_data<int32_t>(ddim, place);
-  } else if (pt.dtype == PaddleDType::FLOAT16) {
+      break;
+    case PaddleDType::FLOAT16:
       input_ptr = t->mutable_data<float16>(ddim, place);
-  } else {
+      break;
+    default:
       LOG(ERROR) << "unsupported feed type " << pt.dtype;
       return false;
   }