'jit.save/load' support save/load function without parameters. (#32430) (#32613)

* jit.save/load support function.

* delete unnittest test_jit_load_model_incomplete.

* edit code according to CI

* Modify the documentation.

* add note to doc.

python/paddle/fluid/dygraph/io.py

@@ -650,6 +650,7 @@ def _construct_params_and_buffers(model_path,
                                   append_suffix=True):
     var_info_filename = str(params_filename) + ".info"
     var_info_path = os.path.join(model_path, var_info_filename)
+    params_path = os.path.join(model_path, str(params_filename))
 
     if os.path.exists(var_info_path):
         var_dict = _load_persistable_vars(model_path, var_info_path,
@@ -671,6 +672,9 @@ def _construct_params_and_buffers(model_path,
             var_dict.update(
                 _load_persistable_vars(model_path, var_info_path, programs[
                     func_name], file_name))
+    elif params_filename is not None and not os.path.exists(params_path):
+        # When saving XX, there is only '*.pdmodel'
+        return dict()
     else:
         var_dict = _load_persistable_vars_by_program(
             model_path, programs['forward'], params_filename)

python/paddle/fluid/dygraph/jit.py

@@ -19,6 +19,7 @@ import pickle
 import warnings
 import functools
 from collections import OrderedDict
+import inspect
 
 import six
 import paddle
@@ -506,7 +507,7 @@ def _build_load_path_and_config(path, config):
 @switch_to_static_graph
 def save(layer, path, input_spec=None, **configs):
     """
-    Saves input Layer as ``paddle.jit.TranslatedLayer``
+    Saves input Layer or function as ``paddle.jit.TranslatedLayer``
     format model, which can be used for inference or fine-tuning after loading.
 
     It will save the translated program and all related persistable
@@ -522,8 +523,12 @@ def save(layer, path, input_spec=None, **configs):
       - ``paddle.static.load_inference_model``
       - Other C++ inference APIs
 
+    .. note::
+        When using ``paddle.jit.save`` to save a function, parameters will not be saved. If you have to 
+        save the parameter, please pass the Layer containing function and parameter to ``paddle.jit.save``.
+
     Args:
-        layer (Layer): The Layer to be saved.
+        layer (Layer|function): The Layer or function to be saved.
         path (str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``.
         input_spec (list[InputSpec|Tensor]|tuple[InputSpec|Tensor], optional): Describes the input of the saved model's forward
             method, which can be described by InputSpec or example Tensor. If None, all input variables of
@@ -543,6 +548,7 @@ def save(layer, path, input_spec=None, **configs):
     Examples:
         .. code-block:: python
 
+            # example 1: save layer
             import numpy as np
             import paddle
             import paddle.nn as nn
@@ -609,6 +615,28 @@ def save(layer, path, input_spec=None, **configs):
             # save
             path = "example_model/linear"
             paddle.jit.save(layer, path)
+
+            # example 2: save function
+            import paddle
+            from paddle.static import InputSpec
+
+
+            def save_function():
+                @paddle.jit.to_static
+                def fun(inputs):
+                    return paddle.tanh(inputs)
+
+                path = 'test_jit_save_load_function_1/func'
+                inps = paddle.rand([3, 6])
+                origin = fun(inps)
+
+                paddle.jit.save(fun, path)
+                load_func = paddle.jit.load(path)
+
+                load_result = load_func(inps)
+                print((load_result - origin).abs().max() < 1e-10)
+                
+            save_function()
     """
 
     # 1. input build & check
@@ -617,9 +645,11 @@ def save(layer, path, input_spec=None, **configs):
         raise RuntimeError(
             "The paddle.jit.save doesn't work when setting ProgramTranslator.enable to False."
         )
-    if not isinstance(layer, Layer):
+
+    if not (isinstance(layer, Layer) or inspect.isfunction(layer) or isinstance(
+            layer, StaticFunction)):
         raise TypeError(
-            "The input layer of paddle.jit.save should be 'Layer', but received layer type is %s."
+            "The input of paddle.jit.save should be 'Layer' or 'Function', but received input type is %s."
             % type(layer))
 
     # NOTE(chenweihang): If the input layer be wrapped by DataParallel,
@@ -647,6 +677,7 @@ def save(layer, path, input_spec=None, **configs):
     # avoid change user given input_spec
     inner_input_spec = None
     if input_spec is not None:
+        if isinstance(layer, Layer):
             for attr_func in dir(inner_layer):
                 static_func = getattr(inner_layer, attr_func, None)
                 if isinstance(static_func,
@@ -654,6 +685,7 @@ def save(layer, path, input_spec=None, **configs):
                     raise ValueError(
                         "If there are static functions other than 'forward' that need to be saved, the input 'input_spec' should be None, but received the type of 'input_spec' is %s."
                         % type(input_spec))
+
         if not isinstance(input_spec, (list, tuple)):
             raise TypeError(
                 "The input input_spec should be 'list', but received input_spec's type is %s."
@@ -674,7 +706,13 @@ def save(layer, path, input_spec=None, **configs):
     configs = _parse_save_configs(configs)
     scope = core.Scope()
     extra_var_info = dict()
-    for attr_func in dir(inner_layer):
+    if isinstance(layer, Layer):
+        functions = dir(inner_layer)
+    else:
+        # layer is function
+        functions = [layer, ]
+    for attr_func in functions:
+        if isinstance(layer, Layer):
             static_func = getattr(inner_layer, attr_func, None)
             if isinstance(static_func, StaticFunction):
                 concrete_program = static_func.concrete_program_specify_input_spec(
@@ -696,25 +734,6 @@ def save(layer, path, input_spec=None, **configs):
             else:
                 continue
 
-        # 3. build input & output of save_infernece_model
-        # NOTE(chenweihang): [ Get input variables name ]
-        # There are two cases, whether to prune the inputs or not
-        # - not prune inputs (recommend):
-        #   - the len(input_spec) == len((concrete_program.inputs) - 1
-        #   - here can use concrete_program.inputs directly
-        # - prune inputs:
-        #   - the input_spec length < len((concrete_program.inputs) - 1
-        #   - the input_spec's name should be in concrete_program.inputs
-        input_var_names = _get_input_var_names(concrete_program.inputs,
-                                               inner_input_spec)
-
-        # NOTE(chenweihang): [ Get output variables ]
-        # the rule is like [ Get input variables name ]. For output var,
-        # we only support VarBase spec, and actually, we only need the
-        # var name of output, and we don't recommended to use output_spec
-        output_vars = _get_output_vars(concrete_program.outputs,
-                                       configs.output_spec)
-
             # NOTE(chenweihang): we maintain the mapping of variable name to
             # structured name, the buffer variable (non-persistable)
             # saved to inference program may not need by dygraph Layer,
@@ -723,11 +742,11 @@ def save(layer, path, input_spec=None, **configs):
             for structured_name, var in six.iteritems(inner_layer.state_dict()):
                 state_names_dict[var.name] = structured_name
 
-        # 4. share parameters from Layer to scope & record var info
+            # 3. share parameters from Layer to scope & record var info
             for param_or_buffer in concrete_program.parameters:
                 # share to scope
-            param_or_buffer_tensor = scope.var(param_or_buffer.name).get_tensor(
-            )
+                param_or_buffer_tensor = scope.var(
+                    param_or_buffer.name).get_tensor()
                 src_tensor = param_or_buffer.value().get_tensor()
                 param_or_buffer_tensor._share_data_with(src_tensor)
                 # record var info
@@ -736,10 +755,42 @@ def save(layer, path, input_spec=None, **configs):
                     if param_or_buffer.name in state_names_dict:
                         extra_info_dict['structured_name'] = state_names_dict[
                             param_or_buffer.name]
-                extra_info_dict['stop_gradient'] = param_or_buffer.stop_gradient
+                    extra_info_dict[
+                        'stop_gradient'] = param_or_buffer.stop_gradient
                     if isinstance(param_or_buffer, ParamBase):
                         extra_info_dict['trainable'] = param_or_buffer.trainable
                     extra_var_info[param_or_buffer.name] = extra_info_dict
+        else:
+            # When layer is a function
+            if isinstance(attr_func, StaticFunction):
+                concrete_program = attr_func.concrete_program_specify_input_spec(
+                    inner_input_spec)
+            else:
+                if inner_input_spec:
+                    inner_input_spec = pack_sequence_as(input_spec,
+                                                        inner_input_spec)
+                static_function = declarative(
+                    attr_func, input_spec=inner_input_spec)
+                concrete_program = static_function.concrete_program
+
+        # 4. build input & output of save_infernece_model
+        # NOTE(chenweihang): [ Get input variables name ]
+        # There are two cases, whether to prune the inputs or not
+        # - not prune inputs (recommend):
+        #   - the len(input_spec) == len((concrete_program.inputs) - 1
+        #   - here can use concrete_program.inputs directly
+        # - prune inputs:
+        #   - the input_spec length < len((concrete_program.inputs) - 1
+        #   - the input_spec's name should be in concrete_program.inputs
+        input_var_names = _get_input_var_names(concrete_program.inputs,
+                                               inner_input_spec)
+
+        # NOTE(chenweihang): [ Get output variables ]
+        # the rule is like [ Get input variables name ]. For output var,
+        # we only support VarBase spec, and actually, we only need the
+        # var name of output, and we don't recommended to use output_spec
+        output_vars = _get_output_vars(concrete_program.outputs,
+                                       configs.output_spec)
 
         # 5. save inference model
         from paddle.fluid.io import save_inference_model
@@ -748,7 +799,7 @@ def save(layer, path, input_spec=None, **configs):
         model_path = dirname
         # NOTE(chenweihang): because prefix contains model and params filename,
         # so we don't support set model_filename & params_filename
-        if 'forward' == attr_func:
+        if 'forward' == attr_func or not isinstance(layer, Layer):
             model_filename = file_prefix + INFER_MODEL_SUFFIX
             params_filename = file_prefix + INFER_PARAMS_SUFFIX
         else:
@@ -782,6 +833,7 @@ def save(layer, path, input_spec=None, **configs):
     # but we can save these information in `jit.save` without changing the original
     # storage to improve user experience. So we save extra information into
     # file `***.pdiparams.info`
+    if isinstance(layer, Layer) and extra_var_info:
         with scope_guard(scope):
             extra_var_info_path = path + INFER_PARAMS_INFO_SUFFIX
             with open(extra_var_info_path, 'wb') as f:

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

@@ -399,15 +399,6 @@ class TestJitSaveLoad(unittest.TestCase):
         with self.assertRaises(ValueError):
             model_dict, _ = fluid.dygraph.load_dygraph(model_path)
 
-    def test_jit_load_model_incomplete(self):
-        model_path = "test_jit_save_load.remove_variables/model"
-        self.train_and_save_model(model_path)
-        # remove `.pdiparams`	
-        var_path = model_path + INFER_PARAMS_SUFFIX
-        os.remove(var_path)
-        with self.assertRaises(ValueError):
-            paddle.jit.load(model_path)
-
     def test_jit_load_no_path(self):
         path = "test_jit_save_load.no_path/model_path"
         with self.assertRaises(ValueError):
@@ -1164,6 +1155,63 @@ class TestJitSaveLoadFinetuneLoad(unittest.TestCase):
         self.assertTrue(float(((result_01 - result_11)).abs().max()) < 1e-5)
 
 
+class TestJitSaveLoadFunction(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+
+    def test_jit_save_load_static_function(self):
+        @paddle.jit.to_static
+        def fun(inputs):
+            return paddle.tanh(inputs)
+
+        path = 'test_jit_save_load_function_1/func'
+        inps = paddle.rand([3, 6])
+        origin = fun(inps)
+
+        paddle.jit.save(fun, path)
+        load_func = paddle.jit.load(path)
+
+        load_result = load_func(inps)
+        self.assertTrue((load_result - origin).abs().max() < 1e-10)
+
+    def test_jit_save_load_function_input_spec(self):
+        @paddle.jit.to_static(input_spec=[
+            InputSpec(
+                shape=[None, 6], dtype='float32', name='x'),
+        ])
+        def fun(inputs):
+            return paddle.nn.functional.relu(inputs)
+
+        path = 'test_jit_save_load_function_2/func'
+        inps = paddle.rand([3, 6])
+        origin = fun(inps)
+
+        paddle.jit.save(fun, path)
+        load_func = paddle.jit.load(path)
+        load_result = load_func(inps)
+        self.assertTrue((load_result - origin).abs().max() < 1e-10)
+
+    def test_jit_save_load_function_function(self):
+        def fun(inputs):
+            return paddle.tanh(inputs)
+
+        path = 'test_jit_save_load_function_3/func'
+        inps = paddle.rand([3, 6])
+        origin = fun(inps)
+
+        paddle.jit.save(
+            fun,
+            path,
+            input_spec=[
+                InputSpec(
+                    shape=[None, 6], dtype='float32', name='x'),
+            ])
+        load_func = paddle.jit.load(path)
+
+        load_result = load_func(inps)
+        self.assertTrue((load_result - origin).abs().max() < 1e-10)
+
+
 class TestJitSaveLoadDataParallel(unittest.TestCase):
     def verify_inference_correctness(self, layer, path):
         layer.eval()