Enhance backward process (#18700)

* prun backward ops
test=develop

paddle/scripts/paddle_build.sh

@@ -480,7 +480,6 @@ function assert_api_spec_approvals() {
     API_FILES=("CMakeLists.txt"
                "paddle/fluid/API.spec"
                "paddle/fluid/op_use_default_grad_op_maker.spec"
-               "python/paddle/fluid/parallel_executor.py"
                "paddle/fluid/framework/operator.h"
                "paddle/fluid/framework/tensor.h"
                "paddle/fluid/framework/details/op_registry.h"
@@ -495,8 +494,11 @@ function assert_api_spec_approvals() {
                "paddle/fluid/framework/ir/graph.h"
                "paddle/fluid/framework/framework.proto"
                "python/requirements.txt"
-               "python/paddle/fluid/compiler.py"
                "python/paddle/fluid/__init__.py"
+               "python/paddle/fluid/compiler.py"
+               "python/paddle/fluid/parallel_executor.py"
+               "python/paddle/fluid/framework.py"
+               "python/paddle/fluid/backward.py"
                "paddle/fluid/operators/distributed/send_recv.proto.in")
     for API_FILE in ${API_FILES[*]}; do
       API_CHANGE=`git diff --name-only upstream/$BRANCH | grep "${API_FILE}" | grep -v "/CMakeLists.txt" || true`

python/paddle/fluid/backward.py

@@ -247,6 +247,125 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
     return op_descs
 
 
+def _find_not_need_ops(grad_op_descs, forward_ops, input_grad_names_set):
+    """
+    Pruning Program with Structural Analysis Method of Computational Graph.
+    The nodes of the computational graph composed of backward OPS should be
+    interconnected. If there are unconnected sub-graphs in the computational graph,
+    these sub-graphs should be cut off.
+
+    Args:
+        grad_op_descs(list[core.OpDesc]): The candidate backward OpDescs.
+        forward_ops(list[Operator]): The forward ops.
+        input_grad_names_set(set): this set is used to store the gradients' name
+            which is generated by backward ops, and input_grad_names_set can help
+            to prune the unnecessary backward ops.
+
+    Return:
+        (list[core.OpDesc]): A list of OpDescs which should be pruned.
+    """
+
+    class Var(object):
+        def __init__(self, var_name):
+            self.var_name = var_name
+            self.gen_op = None
+            self.pendding_ops = []
+
+        def set_gen_op(self, gen_op):
+            assert isinstance(gen_op, Op)
+            assert self.gen_op is None
+            self.gen_op = gen_op
+
+        def add_pending_op(self, op):
+            assert isinstance(op, Op)
+            self.pendding_ops.append(op)
+
+    class Op(object):
+        def __init__(self, op_desc):
+            self.op_desc = op_desc
+            self.inputs = []
+            self.outputs = []
+
+        def insert_input(self, var):
+            assert isinstance(var, Var)
+            self.inputs.append(var)
+
+        def insert_output(self, var):
+            assert isinstance(var, Var)
+            self.outputs.append(var)
+
+    var_versions = dict()
+
+    def _create_node(name):
+        if name not in var_versions.keys():
+            var_versions[name] = [Var(name)]
+        else:
+            var_versions[name].append(Var(name))
+        return var_versions[name][-1]
+
+    def _create_or_get_last_version_node(name):
+        if name not in var_versions.keys():
+            var_versions[name] = [Var(name)]
+        return var_versions[name][-1]
+
+    def _create_op_node(op_desc):
+        op_node = Op(op_desc)
+        for input in op_desc.input_arg_names():
+            var = _create_or_get_last_version_node(name=input)
+            var.add_pending_op(op_node)
+            op_node.insert_input(var)
+        for output in op_desc.output_arg_names():
+            var = _create_node(name=output)
+            var.set_gen_op(op_node)
+            op_node.insert_output(var)
+        return op_node
+
+    # Record the forward vars
+    forward_vars_set = set() if input_grad_names_set is None else set(
+        input_grad_names_set)
+    for op in forward_ops:
+        forward_vars_set.update(op.desc.input_arg_names())
+        forward_vars_set.update(op.desc.output_arg_names())
+
+    # Record the vars which are created during backward and is not generated by op.
+    backward_vars_set = set()
+    # special_op_nodes is the candidate sub-graph head node.
+    special_op_nodes = set()
+    for op_desc in grad_op_descs:
+        input_set = set(op_desc.input_arg_names())
+        # The new_vars are created during backward and is not generated by op.
+        new_vars = input_set - forward_vars_set - backward_vars_set
+        backward_vars_set.update(op_desc.output_arg_names())
+
+        op_node = _create_op_node(op_desc)
+        if len(new_vars) == len(input_set):
+            special_op_nodes.add(op_node)
+
+    not_need_op_descs = []
+    # Start traversing all candidate sub-graph headers to check whether
+    # they are connected to backward computational graphs, and if they are
+    # not, list them in not_need_op_descs
+    for special_op_node in special_op_nodes:
+        op_list = [special_op_node]
+        ready_vars = set(special_op_node.inputs)
+        remove_ops = True
+        candidate_ops = [special_op_node]
+        while len(candidate_ops) > 0:
+            op_node = candidate_ops.pop(0)
+            if _all_in_set_(op_node.inputs, ready_vars):
+                for out_var in op_node.outputs:
+                    candidate_ops.extend(out_var.pendding_ops)
+                    op_list.extend(out_var.pendding_ops)
+                ready_vars.update(op_node.outputs)
+            else:
+                remove_ops = False
+                break
+        if remove_ops:
+            not_need_op_descs.extend([node.op_desc for node in op_list])
+
+    return set(not_need_op_descs)
+
+
 from .proto import framework_pb2
 
 
@@ -276,7 +395,10 @@ def _append_backward_ops_(block,
         grad_to_var(dict)(output argument):
             key(str): grad variable name
             val(str): corresponding forward variable name
-        callback(callable object): a callable object used to decorate new generated grad ops
+        callbacks(callable object): a callable object used to decorate new generated grad ops
+        input_grad_names_set(set): this set is used to store the gradients' name which is
+            generated by backward ops, and input_grad_names_set can help to prune the unnecessary
+            backward ops.
     """
     if callbacks is not None:
         assert (isinstance(callbacks, list))
@@ -342,6 +464,10 @@ def _append_backward_ops_(block,
     grad_op_descs = _remove_no_grad_branch_(grad_op_descs,
                                             no_grad_dict[block.idx])
 
+    not_need_ops = _find_not_need_ops(grad_op_descs, ops, input_grad_names_set)
+    grad_op_descs = [
+        op_desc for op_desc in grad_op_descs if op_desc not in not_need_ops
+    ]
     # append op_desc in grad_op_descs to target_block
     op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName()
     backward = core.op_proto_and_checker_maker.OpRole.Backward

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

@@ -30,6 +30,18 @@ def simple_net1():
     return loss
 
 
+def simple_net2():
+    x = fluid.layers.data(name='image', shape=[784], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    feature = fluid.layers.fc(input=x, size=10, act=None)
+    label = fluid.layers.cast(label, dtype="float32")
+    label = fluid.layers.cast(label, dtype='int64')
+    # Note that the label is not persistable in fluid.layers.cross_entropy.
+    loss = fluid.layers.cross_entropy(input=feature, label=label)
+    loss = fluid.layers.mean(loss)
+    return loss
+
+
 class TestBackward(unittest.TestCase):
     def check_backward(self, model):
         place = fluid.CPUPlace()
@@ -51,6 +63,7 @@ class TestBackward(unittest.TestCase):
 
     def test_backward(self):
         self.check_backward(simple_net1)
+        self.check_backward(simple_net2)
 
 
 if __name__ == '__main__':