add paddle jit save

python/paddle_serving_client/io/__init__.py

@@ -23,7 +23,90 @@ from paddle.fluid.io import save_inference_model
 import paddle.fluid as fluid
 from ..proto import general_model_config_pb2 as model_conf
 import os
+import paddle
+import paddle.nn.functional as F
+from paddle.jit import to_static
 
+def save_dygraph_model(serving_model_folder, client_config_folder, model):
+    paddle.jit.save(model, "serving_tmp")
+    loaded_layer = paddle.jit.load(path=".", model_filename="serving_tmp.pdmodel", params_filename="serving_tmp.pdiparams")
+    feed_target_names = [x.name for x in loaded_layer._input_spec()]
+    fetch_target_names = [x.name for x in loaded_layer._output_spec()]
+
+    inference_program = loaded_layer.program()
+    feed_var_dict = {
+           x: inference_program.global_block().var(x)
+           for x in feed_target_names
+    }
+    fetch_var_dict = {
+           x: inference_program.global_block().var(x)
+           for x in fetch_target_names
+    }
+    config = model_conf.GeneralModelConfig()
+
+    #int64 = 0; float32 = 1; int32 = 2;
+    for key in feed_var_dict:
+        feed_var = model_conf.FeedVar()
+        feed_var.alias_name = key
+        feed_var.name = feed_var_dict[key].name
+        feed_var.is_lod_tensor = feed_var_dict[key].lod_level >= 1
+        if feed_var_dict[key].dtype == core.VarDesc.VarType.INT64:
+            feed_var.feed_type = 0
+        if feed_var_dict[key].dtype == core.VarDesc.VarType.FP32:
+            feed_var.feed_type = 1
+        if feed_var_dict[key].dtype == core.VarDesc.VarType.INT32:
+            feed_var.feed_type = 2
+        if feed_var.is_lod_tensor:
+            feed_var.shape.extend([-1])
+        else:
+            tmp_shape = []
+            for v in feed_var_dict[key].shape:
+                if v >= 0:
+                    tmp_shape.append(v)
+            feed_var.shape.extend(tmp_shape)
+        config.feed_var.extend([feed_var])
+    for key in fetch_var_dict:
+        fetch_var = model_conf.FetchVar()
+        fetch_var.alias_name = key
+        fetch_var.name = fetch_var_dict[key].name
+        fetch_var.is_lod_tensor = 1
+        if fetch_var_dict[key].dtype == core.VarDesc.VarType.INT64:
+            fetch_var.fetch_type = 0
+        if fetch_var_dict[key].dtype == core.VarDesc.VarType.FP32:
+            fetch_var.fetch_type = 1
+        if fetch_var_dict[key].dtype == core.VarDesc.VarType.INT32:
+            fetch_var.fetch_type = 2
+        if fetch_var.is_lod_tensor:
+            fetch_var.shape.extend([-1])
+        else:
+            tmp_shape = []
+            for v in fetch_var_dict[key].shape:
+                if v >= 0:
+                    tmp_shape.append(v)
+            fetch_var.shape.extend(tmp_shape)
+        config.fetch_var.extend([fetch_var])
+    cmd = "mkdir -p {}".format(client_config_folder)
+    os.system(cmd)
+    cmd = "mkdir -p {}".format(serving_model_folder)
+    os.system(cmd)
+    cmd = "mv {} {}/__model__".format("serving_tmp.pdmodel", serving_model_folder)
+    os.system(cmd)
+    cmd = "mv {} {}/__params__".format("serving_tmp.pdiparams", serving_model_folder)
+    os.system(cmd)
+    cmd = "rm -rf serving_tmp.pd*"
+    os.system(cmd)
+    with open("{}/serving_client_conf.prototxt".format(client_config_folder),
+              "w") as fout:
+        fout.write(str(config))
+    with open("{}/serving_server_conf.prototxt".format(serving_model_folder),
+              "w") as fout:
+        fout.write(str(config))
+    with open("{}/serving_client_conf.stream.prototxt".format(
+            client_config_folder), "wb") as fout:
+        fout.write(config.SerializeToString())
+    with open("{}/serving_server_conf.stream.prototxt".format(
+            serving_model_folder), "wb") as fout:
+        fout.write(config.SerializeToString())
 
 def save_model(server_model_folder,
                client_config_folder,
@@ -44,6 +127,8 @@ def save_model(server_model_folder,
         feed_var_names,
         target_vars,
         executor,
+        model_filename="__model__",
+        params_filename="__params__",
         main_program=main_program)
 
     config = model_conf.GeneralModelConfig()