Refine io for test_mnist.py (#40496)

* for test_mnist.py

* remove comments

* using type() replace isinstance()

* valid vars for run program OP in io.py

* open test_mnist in eager_gurad for coverage

python/paddle/fluid/dygraph/io.py

@@ -30,6 +30,7 @@ from paddle.fluid.layers import nn
 from paddle.fluid.layers.utils import _hash_with_id
 from paddle.fluid.dygraph.base import switch_to_static_graph
 from paddle.fluid.framework import in_dygraph_mode
+from paddle import _C_ops
 
 __all__ = ['TranslatedLayer']
 
@@ -761,6 +762,21 @@ def _construct_params_and_buffers(model_path,
     return var_dict
 
 
+def _valid_vars(vars):
+    if vars:
+        return vars
+    if framework._in_eager_mode():
+        return [
+            core.eager.Tensor(core.VarDesc.VarType.FP32, [], "Fake_var",
+                              core.VarDesc.VarType.RAW, False)
+        ]
+    else:
+        return [
+            core.VarBase(core.VarDesc.VarType.FP32, [], "Fake_var",
+                         core.VarDesc.VarType.RAW, False)
+        ]
+
+
 def _run_dygraph(instance, input, program_holder):
 
     # 1. prepare inputs, outputs, attrs
@@ -826,12 +842,7 @@ def _run_dygraph(instance, input, program_holder):
 
     # hold forward variables
     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)
+        tmp_scope_vec = [program_holder.scope]
     else:
         tmp_scope_vec = core.VarBase(core.VarDesc.VarType.FP32, [],
                                      "program_out_scope",
@@ -852,41 +863,18 @@ def _run_dygraph(instance, input, program_holder):
                                var_desc.shape(),
                                var_desc.name(), var_desc.type(), False)
         double_grad_vars.append(var)
-    if len(double_grad_vars) == 0:
-        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
     end_op_index = program_holder.infer_program.block(0).op_size()
-    framework._dygraph_tracer().trace_op(
-        type='run_program',
-        inputs={'X': input_vars,
-                'Params': persistable_vars},
-        outputs={
-            'Out': output_vars,
-            'OutScope': tmp_scope_vec,
-            'DOut': double_grad_vars
-        },
-        attrs={
-            'global_block': trace_program.block(0),
-            'start_op_index': 0,
-            'end_op_index': end_op_index,
-            'is_test': instance._is_test,
-            'program_id': _hash_with_id(trace_program, instance)
-        })
+    attrs = ('global_block', trace_program.block(0), 'start_op_index', 0,
+             'end_op_index', end_op_index, 'is_test', instance._is_test,
+             'program_id', _hash_with_id(trace_program, instance))
+    _C_ops.run_program(
+        _valid_vars(input_vars),
+        _valid_vars(persistable_vars),
+        _valid_vars(output_vars), tmp_scope_vec,
+        _valid_vars(double_grad_vars), *attrs)
     # NOTE: [ why need set param's gradient type here ]
     # if user set sparse gradient mode, the param's gradient
     # will be SelectedRows, not LoDTensor. But tracer will just
@@ -914,8 +902,10 @@ def _run_dygraph(instance, input, program_holder):
 
 def drop_scope_if_no_grad(instance, scope_vec):
     tracer = framework._dygraph_tracer()
+    scope = scope_vec.value().get_scope() if isinstance(scope_vec, (
+        core.VarBase)) else scope_vec[0]
     if (not instance._is_test) and (not tracer._has_grad):
-        scope_vec.value().get_scope().drop_kids()
+        scope.drop_kids()
 
 
 def _run_static_graph(input, program_holder, trace_program):

python/paddle/fluid/dygraph/jit.py

@@ -821,7 +821,7 @@ def save(layer, path, input_spec=None, **configs):
         for var in flatten(input_spec):
             if isinstance(var, paddle.static.InputSpec):
                 inner_input_spec.append(var)
-            elif isinstance(var, (core.VarBase, Variable)):
+            elif isinstance(var, (core.VarBase, core.eager.Tensor, Variable)):
                 inner_input_spec.append(
                     paddle.static.InputSpec.from_tensor(var))
             else:

python/paddle/fluid/dygraph/layers.py

@@ -760,7 +760,8 @@ class Layer(object):
             raise KeyError("The name of buffer can not be empty.")
         elif hasattr(self, name) and name not in self._buffers:
             raise KeyError("attribute '{}' already exists.".format(name))
-        elif tensor is not None and not type(tensor) == core.VarBase:
+        elif tensor is not None and not (type(tensor) == core.VarBase or
+                                         type(tensor) == core.eager.Tensor):
             raise TypeError(
                 "The registered buffer should be a core.VarBase, but received {}.".
                 format(type(tensor).__name__))

python/paddle/fluid/tests/unittests/dygraph_to_static/test_mnist.py

@@ -27,6 +27,7 @@ from paddle.fluid.dygraph.nn import Conv2D, Linear, Pool2D
 from paddle.fluid.optimizer import AdamOptimizer
 from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
 from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
+from paddle.fluid.framework import _test_eager_guard
 
 from predictor_utils import PredictorTools
 
@@ -155,6 +156,13 @@ class TestMNISTWithToStatic(TestMNIST):
             np.allclose(dygraph_loss, static_loss),
             msg='dygraph is {}\n static_res is \n{}'.format(dygraph_loss,
                                                             static_loss))
+        with _test_eager_guard():
+            dygraph_loss = self.train_dygraph()
+            static_loss = self.train_static()
+            self.assertTrue(
+                np.allclose(dygraph_loss, static_loss),
+                msg='dygraph is {}\n static_res is \n{}'.format(dygraph_loss,
+                                                                static_loss))
 
     def test_mnist_declarative_cpu_vs_mkldnn(self):
         dygraph_loss_cpu = self.train_dygraph()

python/paddle/static/input.py

@@ -193,7 +193,7 @@ class InputSpec(object):
                 print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
 
         """
-        if isinstance(tensor, (Variable, core.VarBase)):
+        if isinstance(tensor, (Variable, core.VarBase, core.eager.Tensor)):
             return cls(tensor.shape, tensor.dtype, name or tensor.name)
         else:
             raise ValueError(