[JitLayer]Pybind PEFunction and call phi api in layer_test (PaddlePad…

…dle#44465)

* Support predictor function in JitLayer

* Pybind PEFunction

* Pybind PEFunction and call phi api in layer_test

* Call sqrt phi API

* Polish flags

* Fix comments

paddle/fluid/jit/CMakeLists.txt

@@ -37,6 +37,7 @@ if(WITH_TESTING AND NOT WIN32)
     COMMAND tar zxf multi_program_load.tar.gz)
   set(JIT_DEPS
       phi
+      phi_api
       elementwise_add_op
       matmul_v2_op
       activation_op

paddle/fluid/jit/executor_function.h

@@ -38,6 +38,7 @@ class ExecutorFunction : public BaseFunction {
       : info_(info), place_(place), inner_exe_(place_) {
     utils::ShareParamsIntoScope(info_->ParamNames(), params_dict, &scope_);
     VLOG(6) << framework::GenScopeTreeDebugInfo(&scope_);
+    info_->RemoveDescFeedFetch();
   }
 
   ~ExecutorFunction() noexcept {}

paddle/fluid/jit/function_schema.cc

@@ -62,8 +62,6 @@ FunctionInfo::FunctionInfo(const std::string& func_name,
   for (auto& out_name : program_desc_.GetFetchTargetNames()) {
     schema_.AddOutputArg(out_name);
   }
-  // remove feed fetch op
-  utils::RemoveFeedFetch(&program_desc_);
 }
 
 const std::string& FunctionInfo::FunctionName() const { return func_name_; }
@@ -84,5 +82,9 @@ const std::vector<std::string> FunctionInfo::OutputArgNames() const {
   return schema_.OutputArgNames();
 }
 
+void FunctionInfo::RemoveDescFeedFetch() {
+  utils::RemoveFeedFetch(&program_desc_);
+}
+
 }  // namespace jit
 }  // namespace paddle

paddle/fluid/jit/function_schema.h

@@ -70,6 +70,8 @@ class FunctionInfo {
 
   const std::vector<std::string> OutputArgNames() const;
 
+  void RemoveDescFeedFetch();
+
  private:
   std::string func_name_;
   std::vector<std::string> param_names_;

paddle/fluid/jit/layer_test.cc

@@ -12,17 +12,20 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <cmath>
 #include <string>
 
 #include "gtest/gtest.h"
 
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/variable.h"
+#include "paddle/phi/api/include/api.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/core/tensor_utils.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
 
+#include "paddle/fluid/jit/function_utils.h"
 #include "paddle/fluid/jit/layer.h"
 #include "paddle/fluid/jit/serializer.h"
 
@@ -52,7 +55,7 @@ namespace paddle {
 namespace jit {
 using DenseTensor = phi::DenseTensor;
 
-std::vector<DenseTensor> PrepareInputs(const phi::Place& place) {
+std::vector<Tensor> PrepareInputs(const phi::Place& place) {
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   auto& dev_ctx = *pool.Get(place);
 
@@ -61,7 +64,7 @@ std::vector<DenseTensor> PrepareInputs(const phi::Place& place) {
   t.mutable_data<float>(place);
   phi::funcs::set_constant(dev_ctx, &t, 2.);
 
-  return {t};
+  return utils::ToTensors({t});
 }
 
 TEST(CpuLayerTest, Construct) {
@@ -78,34 +81,38 @@ TEST(CpuLayerTest, Construct) {
   outs = (*func)(inputs);
   out_data = outs[0].data<float>();
   EXPECT_NEAR(out_data[0], 1.41562390, 1e-6);
+  auto pow_out =
+      paddle::experimental::pow(outs[0], paddle::experimental::Scalar(2));
+  out_data = pow_out.data<float>();
+  EXPECT_NEAR(out_data[0], pow(1.41562390, 2.0), 1e-6);
 }
 
 #if defined(PADDLE_WITH_CUDA)
 TEST(GpuLayerTest, Construct) {
   auto place = phi::GPUPlace();
-  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
-  auto& dev_ctx = *pool.Get(place);
-  const auto* dev_ctx_gpu = static_cast<const phi::GPUContext*>(&dev_ctx);
-  DenseTensor cpu_dense_tensor;
 
   std::string path = "./multi_program_load/export";
   auto layer = jit::Load(path, place);
   auto inputs = PrepareInputs(place);
 
   auto outs = layer.forward(inputs);
-  auto out_dense_tensor = outs[0];
-  phi::Copy(
-      *dev_ctx_gpu, out_dense_tensor, phi::CPUPlace(), true, &cpu_dense_tensor);
-  auto out_data = cpu_dense_tensor.data<float>();
+  auto gpu_tensor = outs[0];
+  auto cpu_tensor =
+      paddle::experimental::copy_to(gpu_tensor, phi::CPUPlace(), true);
+  auto out_data = cpu_tensor.data<float>();
   EXPECT_NEAR(out_data[0], 0.02194316, 1e-6);
 
   auto func = layer.Function("infer");
   outs = (*func)(inputs);
-  out_dense_tensor = outs[0];
-  phi::Copy(
-      *dev_ctx_gpu, out_dense_tensor, phi::CPUPlace(), true, &cpu_dense_tensor);
-  out_data = cpu_dense_tensor.data<float>();
+  gpu_tensor = outs[0];
+  cpu_tensor = paddle::experimental::copy_to(gpu_tensor, phi::CPUPlace(), true);
+  out_data = cpu_tensor.data<float>();
   EXPECT_NEAR(out_data[0], 1.41562390, 1e-6);
+
+  auto sqrt_out = paddle::experimental::sqrt(outs[0]);
+  cpu_tensor = paddle::experimental::copy_to(sqrt_out, phi::CPUPlace(), true);
+  out_data = cpu_tensor.data<float>();
+  EXPECT_NEAR(out_data[0], sqrt(1.41562390), 1e-6);
 }
 #endif
 

paddle/fluid/jit/pe_function.h

@@ -39,6 +39,7 @@ class PEFunction : public BaseFunction {
       : info_(info), place_(place) {
     utils::ShareParamsIntoScope(info_->ParamNames(), params_dict, &scope_);
     VLOG(6) << framework::GenScopeTreeDebugInfo(&scope_);
+    info_->RemoveDescFeedFetch();
   }
 
   ~PEFunction() noexcept {}
@@ -51,13 +52,14 @@ class PEFunction : public BaseFunction {
   std::vector<DenseTensor> operator()(const std::vector<DenseTensor> &inputs) {
     std::string prog_string;
     std::hash<std::string> string_hash;
+
     auto &program_desc = info_->ProgramDesc();
     // TODO(dev): Serialize is very slow.
     const_cast<framework::ProgramDesc *>(&program_desc)
         ->Proto()
         ->SerializePartialToString(&prog_string);
-
     int64_t program_id = static_cast<int64_t>(string_hash(prog_string));
+
     const framework::BlockDesc &global_block = program_desc.Block(0);
     int64_t start_op_index = 0;
     int64_t end_op_index = static_cast<int64_t>(global_block.OpSize());
@@ -97,6 +99,8 @@ class PEFunction : public BaseFunction {
     return res;
   }
 
+  const std::shared_ptr<FunctionInfo> &Info() const { return info_; }
+
  private:
   std::shared_ptr<FunctionInfo> info_;
   framework::Scope scope_;

paddle/fluid/jit/serializer.cc

@@ -19,8 +19,11 @@
 #include "paddle/fluid/platform/device_context.h"
 
 #include "paddle/fluid/jit/executor_function.h"
+#include "paddle/fluid/jit/pe_function.h"
 #include "paddle/fluid/jit/serializer_utils.h"
 
+DECLARE_string(jit_engine_type);
+
 namespace paddle {
 namespace jit {
 
@@ -55,9 +58,19 @@ Layer Deserializer::operator()(const std::string& path,
   Layer layer = Layer(infos, params_dict, place);
 
   for (auto& info : infos) {
-    layer.SetFunction(
-        info->FunctionName(),
-        utils::MakeFunction<ExecutorFunction>(info, params_dict, place));
+    if (FLAGS_jit_engine_type == "Executor") {
+      VLOG(3) << "Add function type: ExecutorFunction.";
+      layer.SetFunction(
+          info->FunctionName(),
+          utils::MakeFunction<ExecutorFunction>(info, params_dict, place));
+    } else if (FLAGS_jit_engine_type == "PE") {
+      VLOG(3) << "Add function type: PEFunction.";
+      layer.SetFunction(
+          info->FunctionName(),
+          utils::MakeFunction<PEFunction>(info, params_dict, place));
+    } else {
+      PD_THROW("Invalid JitLayer funciton type.");
+    }
   }
 
   return layer;
@@ -85,7 +98,7 @@ void Deserializer::ReadAttributeData(const std::string& file_path,
                                      Name2VariableMap* attrs_dict) const {}
 
 framework::ProgramDesc Deserializer::LoadProgram(const std::string& file_name) {
-  VLOG(3) << "LoadProgram " << file_name;
+  VLOG(3) << "LoadProgram from: " << file_name;
   std::ifstream fin(file_name, std::ios::in | std::ios::binary);
   fin.seekg(0, std::ios::end);
   std::string buffer(fin.tellg(), ' ');

paddle/fluid/platform/flags.cc

@@ -916,3 +916,18 @@ PADDLE_DEFINE_EXPORTED_bool(
     einsum_opt,
     false,
     "EinsumOp backward will be speedup at the expense of more gpu memory.");
+
+/**
+ * JitLayer related FLAG
+ * Name: FLAGS_jit_engine_type
+ * Since Version: 2.3.0
+ * Value Range: string, {Executor, PE},
+ * default=PE
+ * Example:
+ * Note:
+ * FLAGS_jit_engine_type == Executor, using ExecutorFunction by default
+ * FLAGS_jit_engine_type == PE, using PEFunction by default
+ */
+PADDLE_DEFINE_EXPORTED_string(jit_engine_type,
+                              "PE",
+                              "Choose default funciton type in JitLayer.");

paddle/fluid/pybind/eager_utils.cc

@@ -21,6 +21,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/convert_utils.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/scope_guard.h"
+#include "paddle/fluid/jit/executor_function.h"
+#include "paddle/fluid/jit/pe_function.h"
 #include "paddle/fluid/memory/allocation/allocator.h"
 #include "paddle/fluid/operators/py_func_op.h"
 #include "paddle/fluid/operators/utils.h"
@@ -52,6 +54,7 @@ extern PyTypeObject* g_framework_tensor_pytype;
 extern PyTypeObject* g_framework_lodtensorarray_pytype;
 extern PyTypeObject* g_custom_op_kernel_ctx_pytype;
 extern PyTypeObject* g_executor_function_pytype;
+extern PyTypeObject* g_pe_function_pytype;
 
 int TensorDtype2NumpyDtype(phi::DataType dtype) {
   switch (dtype) {
@@ -234,6 +237,9 @@ std::shared_ptr<jit::BaseFunction> CastPyArg2BaseFunction(PyObject* obj,
           obj, reinterpret_cast<PyObject*>(g_executor_function_pytype))) {
     return ::pybind11::handle(obj)
         .cast<std::shared_ptr<jit::ExecutorFunction>>();
+  } else if (PyObject_IsInstance(
+                 obj, reinterpret_cast<PyObject*>(g_pe_function_pytype))) {
+    return ::pybind11::handle(obj).cast<std::shared_ptr<jit::PEFunction>>();
   } else {
     PADDLE_THROW(platform::errors::InvalidArgument(
         "argument (position %d) must be "

paddle/fluid/pybind/eager_utils.h

@@ -19,7 +19,7 @@ typedef SSIZE_T ssize_t;
 
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/tensor.h"
-#include "paddle/fluid/jit/executor_function.h"
+#include "paddle/fluid/jit/base_function.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/phi/common/backend.h"
 #include "paddle/phi/common/data_type.h"

paddle/fluid/pybind/jit.cc

@@ -21,6 +21,7 @@ limitations under the License. */
 #include "paddle/fluid/jit/executor_function.h"
 #include "paddle/fluid/jit/function_schema.h"
 #include "paddle/fluid/jit/layer.h"
+#include "paddle/fluid/jit/pe_function.h"
 #include "paddle/fluid/jit/serializer.h"
 
 namespace py = pybind11;
@@ -29,6 +30,7 @@ namespace paddle {
 namespace pybind {
 
 PyTypeObject *g_executor_function_pytype = nullptr;
+PyTypeObject *g_pe_function_pytype = nullptr;
 using Variable = paddle::framework::Variable;
 
 void BindJit(pybind11::module *m) {
@@ -44,6 +46,11 @@ void BindJit(pybind11::module *m) {
       reinterpret_cast<PyTypeObject *>(executor_function.ptr());
   executor_function.def("info", &jit::ExecutorFunction::Info);
 
+  py::class_<jit::PEFunction, std::shared_ptr<jit::PEFunction>> pe_function(
+      *m, "PEFunction", R"DOC(PEFunction Class.)DOC");
+  g_pe_function_pytype = reinterpret_cast<PyTypeObject *>(pe_function.ptr());
+  pe_function.def("info", &jit::PEFunction::Info);
+
   py::class_<jit::FunctionInfo, std::shared_ptr<jit::FunctionInfo>>(
       *m, "FunctionInfo", R"DOC(FunctionInfo Class.)DOC")
       .def("name", &jit::FunctionInfo::FunctionName)