[JitLayer]Pybind JitLayer VarBase Function and add python UT

paddle/fluid/jit/CMakeLists.txt

@@ -26,7 +26,8 @@ cc_library(
 cc_library(
   jit_layer
   SRCS layer.cc
-  DEPS jit_compilation_unit)
+  DEPS jit_serializer jit_function_utils jit_serializer_utils
+       jit_compilation_unit jit_function_schema)
 
 if(WITH_TESTING
    AND NOT WIN32
@@ -45,12 +46,7 @@ if(WITH_TESTING
       feed_op
       fetch_op
       scale_op
-      jit_serializer
-      jit_layer
-      jit_function_utils
-      jit_function_schema
-      jit_compilation_unit
-      jit_serializer_utils)
+      jit_layer)
   cc_test(
     layer_test
     SRCS layer_test.cc

paddle/fluid/jit/compilation_unit.cc

@@ -34,5 +34,13 @@ void CompilationUnit::SetFunction(
   function_dict_[name] = function;
 }
 
+std::vector<std::string> CompilationUnit::FunctionNames() const {
+  std::vector<std::string> names;
+  for (auto it = function_dict_.begin(); it != function_dict_.end(); it++) {
+    names.emplace_back(it->first);
+  }
+  return names;
+}
+
 }  // namespace jit
 }  // namespace paddle

paddle/fluid/jit/compilation_unit.h

@@ -32,6 +32,8 @@ class CompilationUnit {
   void SetFunction(const std::string &name,
                    const std::shared_ptr<BaseFunction> &function);
 
+  std::vector<std::string> FunctionNames() const;
+
  private:
   std::unordered_map<std::string, std::shared_ptr<BaseFunction>> function_dict_;
 };

paddle/fluid/jit/executor_function.h

@@ -56,6 +56,8 @@ class ExecutorFunction : 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/layer.cc

@@ -42,5 +42,9 @@ void Layer::SetFunction(const std::string& name,
   unit_.SetFunction(name, function);
 }
 
+std::vector<std::string> Layer::FunctionNames() const {
+  return unit_.FunctionNames();
+}
+
 }  // namespace jit
 }  // namespace paddle

paddle/fluid/jit/layer.h

@@ -50,6 +50,8 @@ class Layer {
   void SetFunction(const std::string& name,
                    const std::shared_ptr<BaseFunction>& function);
 
+  std::vector<std::string> FunctionNames() const;
+
  private:
   // internal::Object obj_;
   Name2VariableMap params_dict_;

paddle/fluid/pybind/CMakeLists.txt

@@ -38,7 +38,8 @@ set(PYBIND_DEPS
     global_utils
     phi_utils
     tcp_store
-    new_profiler)
+    new_profiler
+    jit_layer)
 
 if(WITH_PSCORE)
   set(PYBIND_DEPS ${PYBIND_DEPS} ps_service)

paddle/fluid/pybind/pybind.cc

@@ -96,6 +96,9 @@ limitations under the License. */
 #ifdef PADDLE_WITH_ASCEND
 #include "paddle/fluid/pybind/ascend_wrapper_py.h"
 #endif
+#include "paddle/fluid/jit/executor_function.h"
+#include "paddle/fluid/jit/layer.h"
+#include "paddle/fluid/jit/serializer.h"
 #include "paddle/fluid/pybind/bind_cost_model.h"
 #include "paddle/fluid/pybind/bind_fleet_executor.h"
 #include "paddle/fluid/pybind/box_helper_py.h"
@@ -1743,6 +1746,48 @@ PYBIND11_MODULE(core_noavx, m) {
         return new_rows;
       });
 
+  py::class_<jit::ExecutorFunction, std::shared_ptr<jit::ExecutorFunction>>(
+      m, "ExectorFunction", R"DOC(ExectorFunction Class.)DOC")
+      .def("__call__",
+           [](jit::ExecutorFunction &self,
+              const std::vector<std::shared_ptr<imperative::VarBase>>
+                  &tensor_inputs) {
+             std::vector<Variable> var_inputs;
+             for (auto &tensor : tensor_inputs) {
+               var_inputs.emplace_back(tensor->Var());
+             }
+             auto var_outputs = self(var_inputs);
+
+             std::vector<std::shared_ptr<imperative::VarBase>> tensor_outputs;
+             auto output_names = self.Info()->OutputArgNames();
+             for (size_t i = 0; i < var_outputs.size(); ++i) {
+               auto var = var_outputs[i];
+               std::string name = output_names[i];
+               imperative::VariableWrapper var_wrapper(name, var);
+               auto shared_wrapper =
+                   std::make_shared<imperative::VariableWrapper>(var_wrapper);
+               auto shared_varbase =
+                   std::make_shared<imperative::VarBase>(shared_wrapper);
+               tensor_outputs.emplace_back(shared_varbase);
+             }
+             return tensor_outputs;
+           });
+
+  py::class_<jit::Layer>(m, "Layer", R"DOC(Layer Class.)DOC")
+      .def("function", &jit::Layer::Function)
+      .def("forward", &jit::Layer::forward)
+      .def("function_names", &jit::Layer::FunctionNames);
+
+  m.def("Load",
+        [](const std::string &path, const platform::CPUPlace &cpu_place) {
+          return paddle::jit::Load(path, cpu_place);
+        });
+
+  m.def("Load",
+        [](const std::string &path, const platform::CUDAPlace &cuda_place) {
+          return paddle::jit::Load(path, cuda_place);
+        });
+
   py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
 
 All parameter, weight, gradient are variables in Paddle.

python/paddle/fluid/dygraph/jit.py

@@ -41,13 +41,30 @@
 from paddle.fluid.framework import _current_expected_place, _dygraph_guard, _dygraph_tracer
 from paddle.fluid.framework import dygraph_only, _non_static_mode
 from paddle.fluid.wrapped_decorator import wrap_decorator
+from paddle.fluid.core import Load
 
 __all__ = [
     'TracedLayer', 'declarative', 'dygraph_to_static_func', 'set_code_level',
     'set_verbosity', 'save', 'load', 'not_to_static'
 ]
 
 
+class JitLayer():
+
+    def __init__(self, load_path, place):
+        self.cpp_jit_layer = Load(load_path, place)
+        # bind method
+        for func_name in self.cpp_jit_layer.function_names():
+            setattr(self, func_name, self.bind_funciton(func_name))
+
+    def bind_funciton(self, name):
+
+        def inner_funciton(*args):
+            return self.cpp_jit_layer.function(name)(args)
+
+        return inner_funciton
+
+
 def create_program_from_desc(program_desc):
     program = Program()
     program.desc = program_desc

python/paddle/fluid/tests/unittests/test_jit_layer.py

@@ -0,0 +1,81 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import paddle
+import unittest
+import tempfile
+import numpy as np
+from paddle.static import InputSpec
+from paddle.fluid.framework import _enable_legacy_dygraph
+from paddle.fluid.dygraph.jit import JitLayer
+from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator
+
+_enable_legacy_dygraph()
+paddle.seed(1)
+
+
+class Net(paddle.nn.Layer):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = paddle.nn.Linear(4, 4)
+        self.fc2 = paddle.nn.Linear(4, 4)
+        self._bias = 0.4
+
+    @paddle.jit.to_static(input_spec=[InputSpec([None, 4], dtype='float32')])
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.fc2(out)
+        out = paddle.nn.functional.relu(out)
+        out = paddle.mean(out)
+        return out
+
+    @paddle.jit.to_static(input_spec=[InputSpec([None, 4], dtype='float32')])
+    def infer(self, input):
+        out = self.fc2(input)
+        out = out + self._bias
+        out = paddle.mean(out)
+        return out
+
+
+class TestMultiLoad(unittest.TestCase):
+
+    def test_multi_load(self):
+        self.temp_dir = tempfile.TemporaryDirectory()
+
+        x = paddle.full([2, 4], 2)
+        model = Net()
+        program_translator = ProgramTranslator()
+        program_translator.enable(False)
+        forward_out1 = model.forward(x)
+        infer_out1 = model.infer(x)
+        program_translator.enable(True)
+
+        model_path = os.path.join(self.temp_dir.name, 'multi_program')
+        paddle.jit.save(model, model_path, combine_params=True)
+        place = paddle.CPUPlace()
+        if paddle.is_compiled_with_cuda():
+            place = paddle.CUDAPlace(0)
+        jit_layer = JitLayer(model_path, place)
+        forward_out2 = jit_layer.forward(x)
+        infer_out2 = jit_layer.infer(x)
+        self.assertEqual(np.allclose(forward_out1, forward_out2[0]), True)
+        self.assertEqual(np.allclose(infer_out1, infer_out2[0]), True)
+
+        self.temp_dir.cleanup()
+
+
+if __name__ == '__main__':
+    unittest.main()