[Eager] save load testcase (#39571)

* eager, test=develop

* fix bug, test=develop

* eager, test=develop

* merge legacy to fluid

* eager, test=develop

* eager, test=develop

* Refactor TensorAdd func by template and remove gradient_accumulation in eager

* Remove needless target name

* eager, test=develop

* eager, test=develop

* Use overload instead of template

* Remove legacy code

* Remove legacy code

* selectedrows, test=develop

* Remove DataType test

* eager, test=develop

* eager, test=develop

* support gan, test=develop

* Using Tensor directly instead of using EagerTensor

* support gradient_accumulation

* make test_imperative_lod_tensor_to_selected_rows longer

* make test_imperative_lod_tensor_to_selected_rows longer

* refine code

* ptb, test=develop

* Rename all EagerTensor to Tensor

* Rename some EagerTensor to Tensor

* rename EagerTensor to EagerVariable

* eager, test=develop

* eager, test=develop

* eager, test=develop

* eager, test=develop

* add more test

* eager, test=develop

* Support copiable selected rows and merge develop

* save load, eager, test=develop

* save load, eager, test=develop

* refine, test=develop

* refine, test=develop

* refine, test=develop

* revert static_runner, test=develop

* EagerTensor to Tensor, test=develop

* refine, test=develop

* refine, test=develop

* clear grad, test=develop

* merge, develop

* merge, develop

* merge, test=develop

* merge, test=develop
Co-authored-by: NJiabinYang <360788950@qq.com>
Co-authored-by: NWeilong Wu <veyron_wu@163.com>

paddle/fluid/pybind/eager.cc

@@ -64,12 +64,6 @@ void EmptyTensorInitializer(TensorObject* self, const std::string& name,
                             framework::proto::VarType::Type var_type =
                                 paddle::framework::proto::VarType::LOD_TENSOR) {
   auto ddims = phi::make_ddim(dims);
-  PADDLE_ENFORCE_GE(
-      phi::product(ddims), 0,
-      paddle::platform::errors::InvalidArgument(
-          "Create Eager Tensor with dims contain minus num is ilegal"
-          "Please check your code and make sure you new a "
-          "eager tensor with fixed shape instead of using -1."));
   self->tensor.set_name(name);
   auto autograd_meta = egr::EagerUtils::autograd_meta(&(self->tensor));
   autograd_meta->SetPersistable(persistable);
@@ -83,13 +77,10 @@ void EmptyTensorInitializer(TensorObject* self, const std::string& name,
             phi::make_intrusive<paddle::experimental::SharedStorage>(place),
             phi::DenseTensorMeta(paddle::framework::TransToPtenDataType(dtype),
                                  ddims));
-    dense_tensor->mutable_data(place);
+    if (phi::product(ddims) > 0) {
+      dense_tensor->mutable_data(place);
+    }
     self->tensor.set_impl(dense_tensor);
-  } else {
-    PADDLE_THROW(platform::errors::InvalidArgument(
-        "We only support LoDTensor to be constructed by this initializer, "
-        "please check your var type first and make sure you are going to "
-        "construct LoDTensor."));
   }
 
   if (!autograd_meta->GetMutableGradNode()) {

python/paddle/fluid/dygraph/checkpoint.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import os
 import collections
 import functools
-from ..framework import Variable, default_main_program, in_dygraph_mode, dygraph_only, Parameter, ParamBase, _varbase_creator, _dygraph_tracer
+from ..framework import Variable, default_main_program, in_dygraph_mode, dygraph_only, Parameter, ParamBase, _varbase_creator, _dygraph_tracer, EagerParamBase
 import pickle
 from . import learning_rate_scheduler
 import warnings
@@ -94,7 +94,7 @@ def save_dygraph(state_dict, model_path):
 
     param_num = 0
     for k, v in state_dict.items():
-        if isinstance(v, ParamBase):
+        if isinstance(v, (ParamBase, EagerParamBase)):
             param_num += 1
 
     if param_num == 0:
@@ -103,7 +103,7 @@ def save_dygraph(state_dict, model_path):
     model_dict = {}
     name_table = {}
     for k, v in state_dict.items():
-        if isinstance(v, (Variable, core.VarBase)):
+        if isinstance(v, (Variable, core.VarBase, core.eager.Tensor)):
             model_dict[k] = v.numpy()
             name_table[k] = v.name
         else:

python/paddle/fluid/dygraph/io.py

@@ -535,12 +535,20 @@ def _load_persistable_vars_by_program(model_path,
         orig_each_name = program_holder._suffix_varname_dict[each_var.name()]
         if _is_parameter(each_var, program_holder.infer_program):
             # create output varbase
-            new_var = framework.ParamBase(
-                shape=each_var.shape(),
-                dtype=each_var.dtype(),
-                name=each_var.name(),
-                type=each_var.type(),
-                persistable=True)
+            if framework._in_eager_mode():
+                new_var = framework.EagerParamBase(
+                    shape=each_var.shape(),
+                    dtype=each_var.dtype(),
+                    name=each_var.name(),
+                    type=each_var.type(),
+                    persistable=True)
+            else:
+                new_var = framework.ParamBase(
+                    shape=each_var.shape(),
+                    dtype=each_var.dtype(),
+                    name=each_var.name(),
+                    type=each_var.type(),
+                    persistable=True)
         else:
             new_var = framework._varbase_creator(
                 type=each_var.type(),
@@ -620,11 +628,22 @@ def _load_persistable_vars(model_path, var_info_path, program_holder,
         # create output varbase
         if extra_var_info[name].get('trainable', None) is not None:
             # use default shape and dtype
-            new_var = framework.ParamBase(
-                shape=[1],  # only to pass check, this shape is not meaningful
-                dtype=core.VarDesc.VarType.FP32,
-                name=new_name,
-                persistable=True)
+            if framework._in_eager_mode():
+                new_var = framework.EagerParamBase(
+                    shape=[
+                        1
+                    ],  # only to pass check, this shape is not meaningful
+                    dtype=core.VarDesc.VarType.FP32,
+                    name=new_name,
+                    persistable=True)
+            else:
+                new_var = framework.ParamBase(
+                    shape=[
+                        1
+                    ],  # only to pass check, this shape is not meaningful
+                    dtype=core.VarDesc.VarType.FP32,
+                    name=new_name,
+                    persistable=True)
         else:
             new_var = framework._varbase_creator(
                 name=new_name, persistable=True)
@@ -747,18 +766,26 @@ def _run_dygraph(instance, input, program_holder):
     # 1. prepare inputs, outputs, attrs
     input_vars = []
     for i, value in enumerate(input):
-        if not isinstance(value, (np.ndarray, core.VarBase)):
+        if not isinstance(value, (np.ndarray, core.VarBase, core.eager.Tensor)):
             raise TypeError(
                 "The type of input in TranslatedLayer must be numpy array or Variable(VarBase), but received %s."
                 % type(value))
         # NOTE: In order to unify the API, firstly convert the input to VarBase
         if isinstance(value, np.ndarray):
-            var = core.VarBase(
-                value=value,
-                name=program_holder.input_descs[i].name(),
-                persistable=False,
-                place=framework._current_expected_place(),
-                zero_copy=True)
+            if framework._in_eager_mode():
+                var = core.eager.Tensor(
+                    value=value,
+                    name=program_holder.input_descs[i].name(),
+                    persistable=False,
+                    place=framework._current_expected_place(),
+                    zero_copy=True)
+            else:
+                var = core.VarBase(
+                    value=value,
+                    name=program_holder.input_descs[i].name(),
+                    persistable=False,
+                    place=framework._current_expected_place(),
+                    zero_copy=True)
         else:
             var = value
             # NOTE: we changed var name here, 
@@ -784,30 +811,62 @@ def _run_dygraph(instance, input, program_holder):
 
     output_vars = []
     for var_desc in program_holder.output_descs:
-        var = core.VarBase(var_desc.dtype(),
-                           var_desc.shape(),
-                           var_desc.name(), var_desc.type(), False)
+        if framework._in_eager_mode():
+            var = core.eager.Tensor(
+                dtype=var_desc.dtype(),
+                dims=var_desc.shape(),
+                name=var_desc.name(),
+                type=var_desc.type(),
+                persistable=False)
+        else:
+            var = core.VarBase(var_desc.dtype(),
+                               var_desc.shape(),
+                               var_desc.name(), var_desc.type(), False)
         output_vars.append(var)
 
     # hold forward variables
-    tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [],
-                                 "program_out_scope",
-                                 core.VarDesc.VarType.STEP_SCOPES, True)
+    if framework._in_eager_mode():
+        tmp_scope_vec = core.eager.Tensor(
+            dtype=core.VarDesc.VarType.FP32,
+            dims=[],
+            name="program_out_scope",
+            type=core.VarDesc.VarType.STEP_SCOPES,
+            persistable=True)
+    else:
+        tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [],
+                                     "program_out_scope",
+                                     core.VarDesc.VarType.STEP_SCOPES, True)
     tmp_scope_vec.value().set_scope(program_holder.scope)
 
     double_grad_vars = []
     for var_desc in program_holder.double_grad_descs:
-        var = core.VarBase(var_desc.dtype(),
-                           var_desc.shape(),
-                           var_desc.name(), var_desc.type(), False)
+        if framework._in_eager_mode():
+            var = core.eager.Tensor(
+                dtype=var_desc.dtype(),
+                dims=var_desc.shape(),
+                name=var_desc.name(),
+                type=var_desc.type(),
+                persistable=False)
+        else:
+            var = core.VarBase(var_desc.dtype(),
+                               var_desc.shape(),
+                               var_desc.name(), var_desc.type(), False)
         double_grad_vars.append(var)
     if len(double_grad_vars) == 0:
-        double_grad_vars = [
-            core.VarBase(
-                value=[1],
-                name='Fake_var',
-                place=framework._current_expected_place())
-        ]
+        if framework._in_eager_mode():
+            double_grad_vars = [
+                core.eager.Tensor(
+                    value=[1],
+                    name='Fake_var',
+                    place=framework._current_expected_place())
+            ]
+        else:
+            double_grad_vars = [
+                core.VarBase(
+                    value=[1],
+                    name='Fake_var',
+                    place=framework._current_expected_place())
+            ]
 
     # 2. run program by op
     trace_program = program_holder.infer_program if instance._is_test else program_holder.train_program
@@ -1215,11 +1274,12 @@ class TranslatedLayer(layers.Layer):
         # 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):
+                if isinstance(var,
+                              (framework.ParamBase, framework.EagerParamBase)):
                     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):
+                elif isinstance(var, (core.VarBase, core.eager.Tensor)):
                     dy_name = _generate_unique_var_name(BUFFER_NAME_PREFIX)
                     self._persistable_var_name_dict[name] = dy_name
                     self.register_buffer(dy_name, var)

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

@@ -27,6 +27,7 @@ from test_imperative_base import new_program_scope
 import numpy as np
 import six
 import paddle
+from paddle.fluid.framework import _test_eager_guard
 
 
 class SimpleLSTMRNN(fluid.Layer):
@@ -208,7 +209,7 @@ class PtbModel(fluid.Layer):
 
 
 class TestDygraphPtbRnn(unittest.TestCase):
-    def setUp(self):
+    def func_setUp(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -277,7 +278,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             self.opti_dict = adam.state_dict()
             self.base_opti = {}
             for k, v in self.opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.base_opti[v.name] = v.numpy()
                     self.assertTrue(np.sum(np.abs(v.numpy())) != 0)
                 else:
@@ -294,7 +295,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             fluid.save_dygraph(self.state_dict, "./test_dy")
 
-    def testLoadAndSetVarBase(self):
+    def func_testLoadAndSetVarBase(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -363,7 +364,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             opti_dict = adam.state_dict()
             # set to zero
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_t = v.numpy()
                     var = v.value().get_tensor()
                     var.set(np.zeros_like(np_t), place)
@@ -374,11 +375,12 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 adam._learning_rate.step_num = 0
 
             para_state_dict, opti_state_dict = fluid.load_dygraph("./test_dy")
+            print(opti_state_dict.keys())
             adam.set_state_dict(opti_state_dict)
 
             opti_dict = adam.state_dict()
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.assertTrue(
                         np.array_equal(v.numpy(), self.base_opti[v.name]))
                 else:
@@ -403,7 +405,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetVariable(self):
+    def func_testSetVariable(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -472,7 +474,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             opti_dict = adam.state_dict()
             # set to zero
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_t = v.numpy()
                     var = v.value().get_tensor()
                     var.set(np.zeros_like(np_t), place)
@@ -485,7 +487,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             adam.set_state_dict(self.opti_dict)
             opti_dict = adam.state_dict()
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.assertTrue(
                         np.array_equal(v.numpy(), self.base_opti[v.name]))
                 else:
@@ -510,7 +512,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetNumpy(self):
+    def func_testSetNumpy(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -580,7 +582,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             np_opti_dict = {}
             # set to zero
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_t = v.numpy()
                     np_opti_dict[v.name] = np_t
                     var = v.value().get_tensor()
@@ -596,7 +598,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             opti_dict = adam.state_dict()
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.assertTrue(
                         np.array_equal(v.numpy(), self.base_opti[v.name]))
                 else:
@@ -623,7 +625,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetVariableBeforeTrain(self):
+    def func_testSetVariableBeforeTrain(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -700,7 +702,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testLoadAndSetVarBaseBeforeTrain(self):
+    def func_testLoadAndSetVarBaseBeforeTrain(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -791,7 +793,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetNumpyBeforeTrain(self):
+    def func_testSetNumpyBeforeTrain(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -840,7 +842,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             np_state_dict = {}
 
             for k, v in self.opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_opti_dict[v.name] = v.numpy()
                 else:
                     np_opti_dict[k] = v
@@ -894,7 +896,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testOnlyLoadParams(self):
+    def func_testOnlyLoadParams(self):
         with fluid.dygraph.guard():
             emb = fluid.dygraph.Embedding([10, 10])
             state_dict = emb.state_dict()
@@ -911,7 +913,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             para_state_dict, opti_state_dict = fluid.load_dygraph(
                 os.path.join('saved_dy', 'emb_dy.pdopt'))
 
-    def test_load_compatible_with_keep_name_table(self):
+    def func_test_load_compatible_with_keep_name_table(self):
         with fluid.dygraph.guard():
             emb = fluid.dygraph.Embedding([10, 10])
             state_dict = emb.state_dict()
@@ -922,6 +924,27 @@ class TestDygraphPtbRnn(unittest.TestCase):
             self.assertTrue(para_state_dict != None)
             self.assertTrue(opti_state_dict == None)
 
+    def test_main(self):
+        self.func_setUp()
+        self.func_testLoadAndSetVarBase()
+        self.func_testSetVariable()
+        self.func_testSetNumpy()
+        self.func_testSetVariableBeforeTrain()
+        self.func_testLoadAndSetVarBaseBeforeTrain()
+        self.func_testSetNumpyBeforeTrain()
+        self.func_testOnlyLoadParams()
+        self.func_test_load_compatible_with_keep_name_table()
+        with _test_eager_guard():
+            self.func_setUp()
+            self.func_testLoadAndSetVarBase()
+            self.func_testSetVariable()
+            self.func_testSetNumpy()
+            self.func_testSetVariableBeforeTrain()
+            self.func_testLoadAndSetVarBaseBeforeTrain()
+            self.func_testSetNumpyBeforeTrain()
+            self.func_testOnlyLoadParams()
+            self.func_test_load_compatible_with_keep_name_table()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py

@@ -27,6 +27,7 @@ from test_imperative_base import new_program_scope
 import numpy as np
 import six
 import paddle
+from paddle.fluid.framework import _test_eager_guard
 
 
 class SimpleLSTMRNN(fluid.Layer):
@@ -208,7 +209,7 @@ class PtbModel(fluid.Layer):
 
 
 class TestDygraphPtbRnn(unittest.TestCase):
-    def setUp(self):
+    def func_setUp(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -279,7 +280,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             self.opti_dict = adam.state_dict()
             self.base_opti = {}
             for k, v in self.opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.base_opti[v.name] = v.numpy()
                     self.assertTrue(np.sum(np.abs(v.numpy())) != 0)
                 else:
@@ -296,7 +297,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             paddle.save(self.state_dict, "./test_dy_v2.pdparams")
 
-    def testLoadAndSetVarBase(self):
+    def func_testLoadAndSetVarBase(self):
         self.setUp()
         seed = 90
         hidden_size = 10
@@ -367,7 +368,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             opti_dict = adam.state_dict()
             # set to zero
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_t = v.numpy()
                     var = v.value().get_tensor()
                     var.set(np.zeros_like(np_t), place)
@@ -380,7 +381,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             opti_dict = adam.state_dict()
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.assertTrue(
                         np.array_equal(v.numpy(), self.base_opti[v.name]))
                 else:
@@ -405,7 +406,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetVariable(self):
+    def func_testSetVariable(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -475,7 +476,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             opti_dict = adam.state_dict()
             # set to zero
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_t = v.numpy()
                     var = v.value().get_tensor()
                     var.set(np.zeros_like(np_t), place)
@@ -488,7 +489,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             adam.set_state_dict(self.opti_dict)
             opti_dict = adam.state_dict()
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.assertTrue(
                         np.array_equal(v.numpy(), self.base_opti[v.name]))
                 else:
@@ -513,7 +514,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetNumpy(self):
+    def func_testSetNumpy(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -584,7 +585,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             np_opti_dict = {}
             # set to zero
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_t = v.numpy()
                     np_opti_dict[v.name] = np_t
                     var = v.value().get_tensor()
@@ -600,7 +601,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             opti_dict = adam.state_dict()
             for k, v in opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     self.assertTrue(
                         np.array_equal(v.numpy(), self.base_opti[v.name]))
                 else:
@@ -627,7 +628,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetVariableBeforeTrain(self):
+    def func_testSetVariableBeforeTrain(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -706,7 +707,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testLoadAndSetVarBaseBeforeTrain(self):
+    def func_testLoadAndSetVarBaseBeforeTrain(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -797,7 +798,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testSetNumpyBeforeTrain(self):
+    def func_testSetNumpyBeforeTrain(self):
         seed = 90
         hidden_size = 10
         vocab_size = 1000
@@ -846,7 +847,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             np_state_dict = {}
 
             for k, v in self.opti_dict.items():
-                if isinstance(v, core.VarBase):
+                if isinstance(v, (core.VarBase, core.eager.Tensor)):
                     np_opti_dict[v.name] = v.numpy()
                 else:
                     np_opti_dict[k] = v
@@ -902,7 +903,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
                 base_t = self.model_base[k]
                 self.assertTrue(np.array_equal(new_t, base_t))
 
-    def testOnlyLoadParams(self):
+    def func_testOnlyLoadParams(self):
         with fluid.dygraph.guard():
             emb = fluid.dygraph.Embedding([10, 10])
             state_dict = emb.state_dict()
@@ -911,7 +912,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
             para_state_dict = paddle.load(
                 os.path.join('saved_dy', 'emb_dy.pdparams'))
 
-    def test_no_state_in_input_dict(self):
+    def func_test_no_state_in_input_dict(self):
         with fluid.dygraph.guard():
             emb = fluid.dygraph.Embedding([10, 10])
             state_dict = emb.state_dict()
@@ -923,7 +924,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             emb.set_state_dict(para_state_dict)
 
-    def test_state_shape_mismatch(self):
+    def func_test_state_shape_mismatch(self):
         with fluid.dygraph.guard():
             emb = fluid.dygraph.Embedding([10, 10])
             state_dict = emb.state_dict()
@@ -936,6 +937,29 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
             emb.set_state_dict(para_state_dict)
 
+    def test_main(self):
+        self.func_setUp()
+        self.func_testLoadAndSetVarBase()
+        self.func_testSetVariable()
+        self.func_testSetNumpy()
+        self.func_testSetVariableBeforeTrain()
+        self.func_testLoadAndSetVarBaseBeforeTrain()
+        self.func_testSetNumpyBeforeTrain()
+        self.func_testOnlyLoadParams()
+        self.func_test_no_state_in_input_dict()
+        self.func_test_state_shape_mismatch()
+        with _test_eager_guard():
+            self.func_setUp()
+            self.func_testLoadAndSetVarBase()
+            self.func_testSetVariable()
+            self.func_testSetNumpy()
+            self.func_testSetVariableBeforeTrain()
+            self.func_testLoadAndSetVarBaseBeforeTrain()
+            self.func_testSetNumpyBeforeTrain()
+            self.func_testOnlyLoadParams()
+            self.func_test_no_state_in_input_dict()
+            self.func_test_state_shape_mismatch()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/framework/io.py

@@ -30,7 +30,7 @@ from paddle.fluid.io import _unpack_saved_dict, _pack_loaded_dict, _pickle_loads
 from paddle.fluid.io import _legacy_save as _legacy_static_save
 from paddle.fluid.io import _open_file_buffer, _is_file_path, _is_memory_buffer
 
-from paddle.fluid.framework import Variable, _varbase_creator, _dygraph_tracer, in_dygraph_mode, ParamBase, _current_expected_place, Program
+from paddle.fluid.framework import Variable, _varbase_creator, _dygraph_tracer, in_dygraph_mode, ParamBase, EagerParamBase, _current_expected_place, Program
 from paddle.fluid.dygraph.jit import _SaveLoadConfig
 from paddle.fluid.dygraph.io import _construct_program_holders, _construct_params_and_buffers
 from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX, INFER_PARAMS_INFO_SUFFIX
@@ -42,7 +42,7 @@ def _build_saved_state_dict(state_dict):
     save_dict = {}
     name_table = {}
     for key, value in state_dict.items():
-        if isinstance(value, (Variable, core.VarBase)):
+        if isinstance(value, (Variable, core.VarBase, core.eager.Tensor)):
             if value.type == core.VarDesc.VarType.VOCAB:
                 save_dict[key] = value.value().get_map_tensor()
             else:
@@ -260,6 +260,8 @@ def _pickle_save(obj, f, protocol):
         # This is not a good method, because the pickle module has been modified.
         pickle.dispatch_table[core.VarBase] = reduce_varbase
         pickle.dispatch_table[ParamBase] = reduce_varbase
+        pickle.dispatch_table[core.eager.Tensor] = reduce_varbase
+        pickle.dispatch_table[EagerParamBase] = reduce_varbase
         pickle.dispatch_table[core.LoDTensor] = reduce_LoDTensor
         pickle.dispatch_table.update(dispatch_table_layer)
 
@@ -267,6 +269,8 @@ def _pickle_save(obj, f, protocol):
         pickle.dispatch_table.pop(core.VarBase)
         pickle.dispatch_table.pop(core.LoDTensor)
         pickle.dispatch_table.pop(ParamBase)
+        pickle.dispatch_table.pop(core.eager.Tensor)
+        pickle.dispatch_table.pop(EagerParamBase)
         for k in dispatch_table_layer:
             pickle.dispatch_table.pop(k)
 
@@ -286,6 +290,8 @@ def _pickle_save(obj, f, protocol):
         pickler.dispatch_table[core.VarBase] = reduce_varbase
         pickler.dispatch_table[core.LoDTensor] = reduce_LoDTensor
         pickler.dispatch_table[ParamBase] = reduce_varbase
+        pickler.dispatch_table[core.eager.Tensor] = reduce_varbase
+        pickler.dispatch_table[EagerParamBase] = reduce_varbase
         pickler.dispatch_table.update(dispatch_table_layer)
         pickler.dump(obj)
 
@@ -317,7 +323,8 @@ def _is_state_dict(obj):
 
         def condition(obj):
             return isinstance(obj, (fluid.Layer, Program, core.VarBase,
-                                    core.LoDTensor, core.SelectedRows))
+                                    core.eager.Tensor, core.LoDTensor,
+                                    core.SelectedRows))
 
         # If the value of a dict is a core.VarBase/LoDTensor or a dict 
         # that does not contain a paddle type(Layer, Program, VarBase, LoDTensor, SelectedRows), 
@@ -327,7 +334,8 @@ def _is_state_dict(obj):
                 for k, v in value.items():
                     if _contain_x(v, condition):
                         return False
-            elif not isinstance(value, (core.VarBase, core.LoDTensor)):
+            elif not isinstance(value, (core.VarBase, core.eager.Tensor,
+                                        core.LoDTensor)):
                 return False
         return True
 
@@ -412,8 +420,9 @@ def _parse_every_object(obj, condition_func, convert_func):
     elif type(obj) == set:
         return set(_parse_every_object(list(obj), condition_func, convert_func))
     else:
-        if isinstance(obj, collections.Iterable) and not isinstance(obj, (
-                str, np.ndarray, core.VarBase, core.LoDTensor)):
+        if isinstance(obj, collections.Iterable) and not isinstance(
+                obj,
+            (str, np.ndarray, core.VarBase, core.eager.Tensor, core.LoDTensor)):
             raise NotImplementedError(
                 "The iteratable objects supported are tuple, list, dict, OrderedDict, string. But received {}.".
                 format(type(obj)))
@@ -541,7 +550,7 @@ def _save_binary_var(obj, path):
         _save_lod_tensor(obj, path)
     elif isinstance(obj, core.SelectedRows):
         _save_selected_rows(obj, path)
-    elif isinstance(obj, core.VarBase):
+    elif isinstance(obj, (core.VarBase, core.eager.Tensor)):
         _save_lod_tensor(obj.value().get_tensor(), path)
     else:
         # Since the concept of 'Tensor' is only exposed to users, the error message can only contain tensor instead of 'LoDTensor' or 'SelectedRows'