add a new interface _prune_with_input (#20022)

* add a default value for _prune interface

* modify document

python/paddle/fluid/framework.py

@@ -3597,7 +3597,7 @@ class Program(object):
         p._copy_dist_param_info_from(self)
         return p
 
-    def _prune(self, feeded_var_names, targets):
+    def _prune(self, targets):
         """
         Prune operators and variables which are not needed to generate
         :code:`targets`.
@@ -3611,8 +3611,63 @@ class Program(object):
 
         Returns:
             Program:  A new, pruned program.
+        """
+
+        if not isinstance(targets, list):
+            targets = [targets]
 
+        targets_idx = []
+        for t in targets:
+            if not isinstance(t, Operator):
+                if isinstance(t, Variable):
+                    # After transpiler processing, the op that output this
+                    # variable maybe has been changed, so t.op is not reliable
+                    # and we need to find the current op that generate this
+                    # variable here.
+                    t.op = None
+                    global_block = self.global_block()
+                    for idx, op in enumerate(global_block.ops):
+                        if t.name in op.output_arg_names:
+                            t.op = op
+                            break
+
+                    t = t.op
+                    if t is None:
+                        raise ValueError(
+                            "The target variable must have an "
+                            "associated operator that generates it.")
+                else:
+                    raise ValueError("All targets of prune() can only be "
+                                     "Variable or Operator.")
+
+            targets_idx.append([t.block.idx, t.idx])
+        res = Program()
+        res.desc = core.prune(self.desc, set(), targets_idx)
+        res.blocks = [
+            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
+        ]
+        res._sync_with_cpp()
+        return res
+
+    def _prune_with_input(self, feeded_var_names, targets):
+        """
+        Prune operators and variables which are not needed to generate
+        :code:`targets`. Prune operators and variables which are needed 
+        to generate feeded_var 
+
+        Notes: This is a very low level API. Users should not use this API
+        directly. This API is in flux and not stable.
+
+        Args:
+            feeded_var_names(list|str): A list of variable names from where
+                pruning start. If it is set as [], this API works just like _prune()
+            targets(list|Variable|Operator): A list of variables or operators
+                need to be pruned
+
+        Returns:
+            Program:  A new, pruned program.
         """
+
         if not isinstance(feeded_var_names, list):
             feeded_var_names = [feeded_var_names]
         if not isinstance(targets, list):

python/paddle/fluid/io.py

@@ -1121,7 +1121,8 @@ def save_inference_model(dirname,
 
         main_program.desc.flush()
 
-        main_program = main_program._prune(feeded_var_names, target_vars)
+        main_program = main_program._prune_with_input(
+            feeded_var_names=feeded_var_names, targets=target_vars)
         main_program = main_program._inference_optimize(prune_read_op=True)
         fetch_var_names = [v.name for v in target_vars]
 

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

@@ -0,0 +1,100 @@
+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+
+import paddle.fluid as fluid
+import paddle.fluid.framework as framework
+import paddle.compat as cpt
+
+
+class TestPrune(unittest.TestCase):
+    def net(self):
+        x = fluid.layers.data(name='x', shape=[2], dtype='float32')
+        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+        y = fluid.layers.fc(input=[x], size=2, act="softmax")
+        loss = fluid.layers.cross_entropy(input=y, label=label)
+        loss = fluid.layers.mean(x=loss)
+        return x, y, label, loss
+
+    def test_prune_with_input(self):
+        program = framework.Program()
+        startup_program = framework.Program()
+        block = program.global_block()
+        with fluid.program_guard(program, startup_program):
+            (x, y, label, loss) = self.net()
+        self.assertEqual(len(block.ops), 5)
+        self.assertEqual([op.type for op in block.ops], [
+            "mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
+        ])
+        pruned_program = program._prune_with_input(
+            feeded_var_names=[y.name, label.name], targets=[loss])
+        self.assertEqual(len(pruned_program.global_block().ops), 2)
+        self.assertEqual([op.type for op in pruned_program.global_block().ops],
+                         ["cross_entropy2", "mean"])
+
+    def test_prune(self):
+        program = framework.Program()
+        startup_program = framework.Program()
+        block = program.global_block()
+        with fluid.program_guard(program, startup_program):
+            (x, y, label, loss) = self.net()
+        self.assertEqual(len(block.ops), 5)
+        self.assertEqual([op.type for op in block.ops], [
+            "mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
+        ])
+        pruned_program = program._prune(targets=[loss])
+        self.assertEqual(len(pruned_program.global_block().ops), 5)
+        self.assertEqual(
+            [op.type for op in pruned_program.global_block().ops],
+            ["mul", "elementwise_add", "softmax", "cross_entropy2", "mean"])
+
+    def test_prune_target_not_list(self):
+        program = framework.Program()
+        startup_program = framework.Program()
+        block = program.global_block()
+        with fluid.program_guard(program, startup_program):
+            (x, y, label, loss) = self.net()
+        self.assertEqual(len(block.ops), 5)
+        self.assertEqual([op.type for op in block.ops], [
+            "mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
+        ])
+        pruned_program = program._prune(targets=loss)
+        self.assertEqual(len(pruned_program.global_block().ops), 5)
+        self.assertEqual(
+            [op.type for op in pruned_program.global_block().ops],
+            ["mul", "elementwise_add", "softmax", "cross_entropy2", "mean"])
+
+    def test_prune_target_none(self):
+        program = framework.Program()
+        startup_program = framework.Program()
+        block = program.global_block()
+        with fluid.program_guard(program, startup_program):
+            (x, y, label, loss) = self.net()
+        self.assertEqual(len(block.ops), 5)
+        self.assertEqual([op.type for op in block.ops], [
+            "mul", "elementwise_add", "softmax", "cross_entropy2", "mean"
+        ])
+        try:
+            pruned_program = program._prune(targets=None)
+        except ValueError as e:
+            self.assertEqual(
+                "All targets of prune() can only be Variable or Operator.",
+                cpt.get_exception_message(e))
+
+
+if __name__ == '__main__':
+    unittest.main()