[cherry-pick]polish no_grad_set of gradient and append_backward (#22440) (#22498)

python/paddle/fluid/backward.py

@@ -1020,6 +1020,26 @@ def _get_son_parent_block_idx_dict(program, current_block_idx):
     return son_parent_block_idx_dict
 
 
+def _get_no_grad_set_name(no_grad_set):
+    no_grad_set_name = set()
+    if no_grad_set is not None:
+        if isinstance(no_grad_set, (set, list, tuple)):
+            for i, no_grad_var in enumerate(no_grad_set):
+                if isinstance(no_grad_var, framework.Variable):
+                    no_grad_set_name.add(no_grad_var.name)
+                elif isinstance(no_grad_var, six.string_types):
+                    no_grad_set_name.add(no_grad_var)
+                else:
+                    raise TypeError(
+                        "The type of no_grad_set's member must be paddle.fluid.Variable or str, but received %s."
+                        % (type(no_grad_var)))
+        else:
+            raise TypeError(
+                "The type of no_grad_set should be set or list or tuple, but received {}".
+                format(type(no_grad_set)))
+    return no_grad_set_name
+
+
 def append_backward(loss,
                     parameter_list=None,
                     no_grad_set=None,
@@ -1043,11 +1063,11 @@ def append_backward(loss,
                                            If it is None, all parameters
                                            will be updated.
                                            Default: None.
-        no_grad_set(set[str], optional): Variable names in the :ref:`api_guide_Block_en` 0 whose gradients
+        no_grad_set(set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients
                                should be ignored. All variables with
                                `stop_gradient=True` from all blocks will
                                be automatically added into this set.
-                               If this parameter is not None, the names in this set will be added to the default set.
+                               If this parameter is not None, the Variables or Variable.names in this set will be added to the default set.
                                Default: None.
         callbacks(list[callable object], optional): List of callback functions.
                                                The callbacks are used for
@@ -1084,18 +1104,40 @@ def append_backward(loss,
         .. code-block:: python
 
             import paddle.fluid as fluid
-            x = fluid.data(name='x', shape=[None, 13], dtype='float32')
-            y = fluid.data(name='y', shape=[None, 1], dtype='float32')
 
-            y_predict = fluid.layers.fc(input=x, size=1, act=None)
+            x = fluid.data(name='x', shape=[None, 13], dtype='int64')
+            y = fluid.data(name='y', shape=[None, 1], dtype='float32')
+            x_emb = fluid.embedding(x, size=[100, 256])
+            y_predict = fluid.layers.fc(input=x_emb, size=1, act=None, name='my_fc')
             loss = fluid.layers.square_error_cost(input=y_predict, label=y)
-
             avg_loss = fluid.layers.mean(loss)
-            param_grad_list = fluid.backward.append_backward(loss=avg_loss)
-            p_g_list1 = fluid.backward.append_backward(loss=avg_loss)  # len(p_g_list1) == 2
-            p_g_list2 = fluid.backward.append_backward(loss=avg_loss, parameter_list=[p_g_list1[0][0].name])  # len(p_g_list1) == 1
-            p_g_list3 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set([p_g_list1[0][0].name]))  # len(p_g_list1) == 1
-            p_g_list4 = fluid.backward.append_backward(loss=avg_loss, parameter_list=[p_g_list1[0][0].name], no_grad_set=set([p_g_list1[0][0].name]))  # len(p_g_list1) == 0
+
+            # Get all weights in main_program, not include bias.
+            all_weights = [param for param in fluid.default_main_program().block(0).all_parameters() if 'w_' in param.name]
+            all_weights_name = [w.name for w in all_weights]
+
+            # return all param_grads needed to be updated if parameter_list set default None.
+            p_g_list1 = fluid.backward.append_backward(loss=avg_loss)
+            # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD), (my_fc.b_0, my_fc.b_0@GRAD)]
+
+            # return the param_grads corresponding to parameter_list that can be list of param (Variable).
+            p_g_list2 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights)
+            # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)]
+
+            # parameter_list can be list of param.name (str).
+            p_g_list3 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights_name)
+            # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)]
+
+            # no_grad_set can be set of Variables that means grad will be cut off from these Variables.
+            p_g_list4 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set([x_emb]))
+            # output: [(my_fc.w_0, my_fc.w_0@GRAD), (my_fc.b_0, my_fc.b_0@GRAD)]
+
+            # no_grad_set can be set of Variable.name when the Variable is created inside layers and can't be specified explicitly.
+            p_g_list5 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set(['my_fc.b_0']))
+            # output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)]
+
+            # return [] because all param_grads are filtered by no_grad_set.
+            p_g_list6 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights, no_grad_set=set(all_weights))
 
     """
     assert isinstance(loss, framework.Variable)
@@ -1125,7 +1167,8 @@ def append_backward(loss,
 
     if no_grad_set is None:
         no_grad_set = set()
-    no_grad_set = copy.copy(no_grad_set)
+    else:
+        no_grad_set = _get_no_grad_set_name(copy.copy(no_grad_set))
     no_grad_dict = _get_stop_gradients_(program)
     # no_grad_set only contains vars in block 0
     # Todo(liym27): support vars in sub block
@@ -1411,12 +1454,15 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
     Args:
         targets(Variable|list[Variable]): The target variables
         inputs(Variable|list[Variable]): The input variables
-        target_gradients (Variable|list[Variable]|None): The gradient variables
+        target_gradients (Variable|list[Variable], optional): The gradient variables
             of targets which has the same shape with targets, If None, ones will
             be created for them.
-        no_grad_set(set[string]): The names of variables that have no gradients
-            in Block 0. All variables with `stop_gradient=True` from all blocks
-            will be automatically added.
+        no_grad_set(set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients
+                               should be ignored. All variables with
+                               `stop_gradient=True` from all blocks will
+                               be automatically added into this set.
+                               If this parameter is not None, the Variables or Variable.names in this set will be added to the default set.
+                               Default: None.
 
     Return:
         (list[Variable]): A list of gradients for inputs
@@ -1442,7 +1488,8 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
 
     if no_grad_set is None:
         no_grad_set = set()
-    no_grad_set = copy.copy(no_grad_set)
+    else:
+        no_grad_set = _get_no_grad_set_name(copy.copy(no_grad_set))
     no_grad_dict = _get_stop_gradients_(prog)
     no_grad_dict[0].update(list(map(_append_grad_suffix_, no_grad_set)))
 
@@ -1533,12 +1580,13 @@ def gradients(targets, inputs, target_gradients=None, no_grad_set=None):
     Args:
         targets (Variable|list[Variable]): The target variables.
         inputs (Variable|list[Variable]): The input variables.
-        target_gradients (Variable|list[Variable]|None): The gradient variables
+        target_gradients (Variable|list[Variable], optional): The gradient variables
             of targets which has the same shape with targets, If None, ones will
             be created for them.
-        no_grad_set (set[string]): The names of variables that have no gradients
-            in Block 0. All variables with `stop_gradient=True` from all blocks
-            will be automatically added.
+        no_grad_set (set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients
+            should be ignored. All variables with `stop_gradient=True` from all blocks will
+            be automatically added into this set. If this parameter is not None, the Variables or Variable.names
+            in this set will be added to the default set. Default: None.
 
     Return:
         (list[Variable]): A list of gradients for inputs
@@ -1550,7 +1598,7 @@ def gradients(targets, inputs, target_gradients=None, no_grad_set=None):
 
             import paddle.fluid as fluid
 
-            x = fluid.layers.data(name='x', shape=[2,8,8], dtype='float32')
+            x = fluid.data(name='x', shape=[None,2,8,8], dtype='float32')
             x.stop_gradient=False
             y = fluid.layers.conv2d(x, 4, 1, bias_attr=False)
             y = fluid.layers.relu(y)

python/paddle/fluid/optimizer.py

@@ -23,7 +23,7 @@ from paddle.fluid.framework import Program, Variable, name_scope, default_main_p
 from . import framework
 from . import layers
 from . import unique_name
-from .backward import append_backward, _some_in_set_, _append_grad_suffix_
+from .backward import append_backward, _some_in_set_, _append_grad_suffix_, _get_no_grad_set_name
 from .clip import append_gradient_clip_ops, error_clip_callback
 from .framework import program_guard
 from .initializer import Constant
@@ -599,7 +599,7 @@ class Optimizer(object):
             parameter_list (list, optional): List of ``Variable`` or ``Variable.name`` to update
                 to minimize ``loss``. The default value is None, at this time all parameters
                 will be updated.
-            no_grad_set (set, optional): Set of ``Variable`` objects that don't need
+            no_grad_set (set, optional): Set of ``Variable``  or ``Variable.name`` that don't need
                 to be updated. The default value is None.
             callbacks (list, optional): list of callable objects to run when appending backward
                 operator for one parameter. The default value is None.
@@ -712,14 +712,7 @@ class Optimizer(object):
         return optimize_ops
 
     def _get_no_grad_set(self, loss, no_grad_set=None):
-        if no_grad_set is None:
-            no_grad_set = set()
-        elif isinstance(no_grad_set, set) or isinstance(
-                no_grad_set, list) or isinstance(no_grad_set, tuple):
-            no_grad_set = set(no_grad_set)
-        else:
-            assert "no_grad_set should be a set, but the passed type is {}".format(
-                type(no_grad_set))
+        no_grad_set = _get_no_grad_set_name(no_grad_set)
         parameters = loss.block.program.global_block().all_parameters()
         param_no_trainable = set(
             [param.name for param in parameters if param.trainable is False])
@@ -777,7 +770,7 @@ class Optimizer(object):
             parameter_list (list, optional): List of ``Variable`` or ``Variable.name`` to update
                 to minimize ``loss``. The default value is None, at this time all parameters
                 will be updated.
-            no_grad_set (set, optional): Set of ``Variable`` objects that don't need
+            no_grad_set (set, optional): Set of ``Variable``  or ``Variable.name`` that don't need
                 to be updated. The default value is None.
             grad_clip (GradClipBase, optional) : Gradient clipping strategy, static
                 graph mode does not need to use this argument. Currently, this argument
@@ -3850,8 +3843,8 @@ class RecomputeOptimizer(Optimizer):
             loss (Variable): loss variable to run optimizations.
             startup_program (Program): startup_program for initializing parameters
                 in `parameter_list`.
-            parameter_list (list): list of Variables to update.
-            no_grad_set (set|None): set of Variables should be ignored.
+            parameter_list (list): list of Variables or Variable.names to update.
+            no_grad_set (set|None): set of Variables or Variables.names should be ignored.
             callbacks (list|None): list of callables to run when appending backward
                 operator for one parameter.
             checkpoints (list): list of Variables as checkpoints

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

@@ -142,6 +142,21 @@ class TestBackward(unittest.TestCase):
             exe.run(startup)
             exe.run(feed=net.init_data())
 
+    def _check_error_no_grad_set(self, net, no_grad_set):
+        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        main = fluid.Program()
+        startup = fluid.Program()
+
+        with fluid.program_guard(main, startup):
+            loss = net.build_model()
+            optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+            optimizer.minimize(loss, no_grad_set=no_grad_set)
+            exe.run(startup)
+            exe.run(feed=net.init_data())
+
 
 class SimpleNet(BackwardNet):
     def __init__(self):
@@ -233,12 +248,25 @@ class TestSimpleNetWithErrorParamList(TestBackward):
         # The type of parameter_list argument must be list or tuple
         with self.assertRaises(TypeError):
             self._check_error_param_list(self.net, "test")
-        # The type of parameter_list's member must be varable or str
+        # The type of parameter_list's member must be Variable or str
         test = fluid.data(name='test', shape=[None, 90], dtype='float32')
         with self.assertRaises(TypeError):
             self._check_error_param_list(self.net, [test, "test", 3])
 
 
+class TestSimpleNetWithErrorNoGradSet(TestBackward):
+    def test_no_grad_set_type_error(self):
+        self.global_block_idx = 0
+        self.net = SimpleNet()
+        # The type of no_grad_set argument must be set or list or tuple
+        with self.assertRaises(TypeError):
+            self._check_error_no_grad_set(self.net, "test")
+        # The type of no_grad_set's member must be Variable or str
+        test = fluid.data(name='test', shape=[None, 90], dtype='float32')
+        with self.assertRaises(TypeError):
+            self._check_error_no_grad_set(self.net, [test, "test", 3])
+
+
 # TODO(Aurelius84): add conditional network test
 class ConditionalNet(BackwardNet):
     def __init__(self):

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

@@ -55,7 +55,7 @@ class TestFusedEmbeddingSeqPoolOp(OpTest):
         if ver.mkl() == "ON" and 'Linux' in platform.platform():
             self.attrs = {'is_sparse': False}
             self.check_grad(
-                ['W'], 'Out', no_grad_set=('Ids'), check_dygraph=False)
+                ['W'], 'Out', no_grad_set=['Ids'], check_dygraph=False)
 
 
 class TestLookupTableOpWithPadding(TestFusedEmbeddingSeqPoolOp):
@@ -89,7 +89,7 @@ class TestLookupTableOpWithPadding(TestFusedEmbeddingSeqPoolOp):
             self.attrs = {'padding_idx': int(padding_idx), 'is_sparse': False}
             # TODO(wangzhongpu): support lod in dygraph mode
             self.check_grad(
-                ['W'], 'Out', no_grad_set=('Ids'), check_dygraph=False)
+                ['W'], 'Out', no_grad_set=['Ids'], check_dygraph=False)
 
 
 class TestFusedEmbeddingSeqPoolApi(unittest.TestCase):