Refine jit load model by extra_var_info (#26461)

* refine load model by extra_var_info

* polish unittest for coverage

python/paddle/fluid/dygraph/io.py

@@ -437,8 +437,16 @@ def _load_persistable_vars(model_path,
         value: key
         for key, value in program_holder._suffix_varname_dict.items()
     }
-    # NOTE: some var may not be Parameter
-    for name in sorted(extra_var_info):
+
+    # NOTE(chenweihang): we need load persistable vars based the program,
+    # because the program may be pruned when `save_inference_model`, some
+    # var in `extra_var_info` may have been pruned 
+    for name in sorted(inv_suffix_varname_dict):
+        if name not in extra_var_info:
+            raise RuntimeError(
+                "The model to be loaded is not complete."
+                "The variable `%s` of program cannot be found in loaded model.",
+                name)
         # get suffix var name, see [why need to append suffix to persistable vars]
         new_name = inv_suffix_varname_dict[name]
         # create output varbase
@@ -641,19 +649,21 @@ class TranslatedLayer(layers.Layer):
         # name contains `.` originally, such as `linear_0.w_0`, so here
         # need to generate new var name for each var
         self._persistable_var_name_dict = dict()
-        for name, var in persistable_vars.items():
-            if isinstance(var, framework.ParamBase):
-                dy_name = _generate_unique_var_name(PARAMETER_NAME_PREFIX)
-                self._persistable_var_name_dict[name] = dy_name
-                self.add_parameter(dy_name, var)
-            elif isinstance(var, core.VarBase):
-                dy_name = _generate_unique_var_name(BUFFER_NAME_PREFIX)
-                self._persistable_var_name_dict[name] = dy_name
-                self.register_buffer(dy_name, var)
-            else:
-                raise TypeError(
-                    "Adding persistent variable which  to layer is not supported now"
-                )
+        # the TranslatedLayer object holded var names count started from 0
+        with unique_name.guard():
+            for name, var in persistable_vars.items():
+                if isinstance(var, framework.ParamBase):
+                    dy_name = _generate_unique_var_name(PARAMETER_NAME_PREFIX)
+                    self._persistable_var_name_dict[name] = dy_name
+                    self.add_parameter(dy_name, var)
+                elif isinstance(var, core.VarBase):
+                    dy_name = _generate_unique_var_name(BUFFER_NAME_PREFIX)
+                    self._persistable_var_name_dict[name] = dy_name
+                    self.register_buffer(dy_name, var)
+                else:
+                    raise TypeError(
+                        "Adding persistent variable which  to layer is not supported now"
+                    )
 
         self._is_test = True
 

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

@@ -15,6 +15,7 @@
 from __future__ import print_function
 
 import os
+import pickle
 import unittest
 import numpy as np
 
@@ -25,7 +26,7 @@ from paddle.fluid.dygraph import declarative, ProgramTranslator
 from paddle.fluid.dygraph.io import VARIABLE_FILENAME, EXTRA_VAR_INFO_FILENAME
 
 BATCH_SIZE = 32
-BATCH_NUM = 20
+BATCH_NUM = 10
 SEED = 10
 
 
@@ -318,5 +319,76 @@ class TestJitMultipleLoading(unittest.TestCase):
             name_set.add(var.name)
 
 
+class LinearNetReturnHidden(fluid.dygraph.Layer):
+    def __init__(self, in_size, out_size):
+        super(LinearNetReturnHidden, self).__init__()
+        self._linear_1 = Linear(in_size, out_size)
+        self._linear_2 = Linear(in_size, out_size)
+
+    @declarative
+    def forward(self, x):
+        y = self._linear_1(x)
+        z = self._linear_2(y)
+        loss = fluid.layers.mean(z)
+        return y, loss
+
+
+class TestJitPruneModelAndLoad(unittest.TestCase):
+    def setUp(self):
+        self.linear_size = 4
+        self.model_path = "model.jit_prune_model_and_load"
+        # enable dygraph mode
+        fluid.enable_dygraph()
+        # config seed
+        fluid.default_main_program().random_seed = SEED
+
+    def train_and_save(self):
+        train_layer = LinearNetReturnHidden(8, 8)
+        adam = fluid.optimizer.AdamOptimizer(
+            learning_rate=0.1, parameter_list=train_layer.parameters())
+        x = fluid.dygraph.to_variable(
+            np.random.random((4, 8)).astype('float32'))
+        for i in range(10):
+            hidden, loss = train_layer(x)
+            loss.backward()
+            adam.minimize(loss)
+            train_layer.clear_gradients()
+
+        configs = fluid.dygraph.jit.SaveLoadConfig()
+        configs.output_spec = [hidden]
+        fluid.dygraph.jit.save(
+            layer=train_layer,
+            model_path=self.model_path,
+            input_spec=[x],
+            configs=configs)
+
+        return train_layer
+
+    def test_load_pruned_model(self):
+        train_layer = self.train_and_save()
+        train_layer.eval()
+
+        infer_layer = fluid.dygraph.jit.load(self.model_path)
+
+        x = fluid.dygraph.to_variable(
+            np.random.random((4, 8)).astype('float32'))
+        self.assertTrue(
+            np.array_equal(train_layer(x)[0].numpy(), infer_layer(x).numpy()))
+
+    def test_load_var_not_in_extra_var_info(self):
+        self.train_and_save()
+
+        # chage extra var info
+        var_info_path = os.path.join(self.model_path, EXTRA_VAR_INFO_FILENAME)
+        with open(var_info_path, 'rb') as f:
+            extra_var_info = pickle.load(f)
+            extra_var_info.clear()
+        with open(var_info_path, 'wb') as f:
+            pickle.dump(extra_var_info, f, protocol=2)
+
+        with self.assertRaises(RuntimeError):
+            fluid.dygraph.jit.load(self.model_path)
+
+
 if __name__ == '__main__':
     unittest.main()