add memory optimization transpiler (#7356)

python/paddle/v2/fluid/__init__.py

@@ -19,12 +19,13 @@ from data_feeder import DataFeeder
 from core import LoDTensor, CPUPlace, CUDAPlace
 from distribute_transpiler import DistributeTranspiler
 import clip
+from memory_optimization_transpiler import memory_optimize
 
 Tensor = LoDTensor
 __all__ = framework.__all__ + executor.__all__ + [
     'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
     'regularizer', 'LoDTensor', 'CPUPlace', 'CUDAPlace', 'Tensor', 'ParamAttr'
-    'DataFeeder', 'clip', 'DistributeTranspiler'
+    'DataFeeder', 'clip', 'DistributeTranspiler', 'memory_optimize'
 ]
 
 

python/paddle/v2/fluid/framework.py

@@ -773,6 +773,9 @@ class Program(object):
         proto = framework_pb2.ProgramDesc.FromString(str(protostr))
         return _debug_string_(proto, throw_on_error)
 
+    def get_desc(self):
+        return self.desc
+
     def clone(self):
         p = Program()
         p.desc = core.ProgramDesc(self.desc)

python/paddle/v2/fluid/memory_optimization_transpiler.py

@@ -0,0 +1,115 @@
+from collections import defaultdict
+import framework
+from framework import Program, default_main_program, Parameter, Variable
+import backward
+from backward import _rename_arg_
+
+
+class ControlFlowGraph(object):
+    def __init__(self, Program):
+        self._program = Program
+        self._succesors = defaultdict(set)
+        self._presucessors = defaultdict(set)
+        self._uses = defaultdict(set)
+        self._defs = defaultdict(set)
+        self._live_in = defaultdict(set)
+        self._live_out = defaultdict(set)
+
+    def _add_connections(self, connections):
+        for node1, node2 in connections:
+            self._add(node1, node2)
+
+    def _add(self, node1, node2):
+        self._succesors[node1].add(node2)
+        self._presucessors[node2].add(node1)
+
+    def _build_graph(self):
+        program_desc = self._program.get_desc()
+        block_size = program_desc.num_blocks()
+
+        # TODO(qijun) handle Program with if/while operators
+        self.global_block = program_desc.block(0)
+        self.op_size = self.global_block.op_size()
+
+        op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
+        self._add_connections(op_node_connections)
+
+        self.ops = [self.global_block.op(i) for i in range(self.op_size)]
+
+        for i in range(self.op_size):
+            self._uses[i].update(self.ops[i].input_arg_names())
+            self._defs[i].update(self.ops[i].output_arg_names())
+
+    def _reach_fixed_point(self, live_in, live_out):
+        if len(live_in) != len(self._live_in):
+            return False
+        if len(live_out) != len(self._live_out):
+            return False
+        for i in range(self.op_size):
+            if live_in[i] != self._live_in[i]:
+                return False
+        for i in range(self.op_size):
+            if live_out[i] != self._live_out[i]:
+                return False
+        return True
+
+    def _dataflow_analyze(self):
+        self._build_graph()
+        live_in = defaultdict(set)
+        live_out = defaultdict(set)
+        while True:
+            for i in range(self.op_size):
+                live_in[i] = set(self._live_in[i])
+                live_out[i] = set(self._live_out[i])
+                self._live_in[i] = self._uses[i] | (
+                    self._live_out[i] - self._defs[i])
+                for s in self._succesors[i]:
+                    self._live_out[i] |= self._live_in[s]
+
+            if self._reach_fixed_point(live_in, live_out):
+                break
+
+    def _get_diff(self, a, b):
+        u = a & b
+        return a - u, b - u
+
+    def memory_optimize(self):
+        self._build_graph()
+        self._dataflow_analyze()
+        self.pool = []
+        for i in range(self.op_size):
+            if self.pool:
+                out_pair = [(x, self.global_block.var(str(x)).shape())
+                            for x in self._defs[i]]
+                for x, x_shape in out_pair:
+                    for index, cache_pair in enumerate(self.pool):
+                        cache_var = cache_pair[0]
+                        cache_shape = cache_pair[1]
+                        if x_shape == cache_shape:
+                            print(
+                                "Hit Cache !!!! cache pool index is %d, var name is %s, cached var name is %s, var shape is %s "
+                                % (index, x, cache_var, str(cache_shape)))
+                            self.pool.pop(index)
+                            _rename_arg_(self.ops, x, cache_var, begin_idx=i)
+                            self._dataflow_analyze()
+                            break
+
+            in_diff, out_diff = self._get_diff(self._live_in[i],
+                                               self._live_out[i])
+            can_optimize = filter(
+                lambda x: not self.global_block.var(str(x)).persistable(),
+                in_diff)
+            if can_optimize:
+                for var_name in can_optimize:
+                    self.pool.append((
+                        var_name, self.global_block.var(str(var_name)).shape()))
+
+    def get_program(self):
+        return self._program
+
+
+def memory_optimize(input_program):
+    graph = ControlFlowGraph(input_program)
+    graph.memory_optimize()
+    result_program = graph.get_program()
+    return result_program

python/paddle/v2/fluid/tests/test_memory_optimization_transpiler.py

@@ -0,0 +1,33 @@
+from __future__ import print_function
+import unittest
+
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.optimizer as optimizer
+from paddle.v2.fluid.framework import Program, program_guard
+from paddle.v2.fluid.memory_optimization_transpiler import memory_optimize
+
+
+class TestControlFlowGraph(unittest.TestCase):
+    def setUp(self):
+        program = Program()
+        with program_guard(program, startup_program=Program()):
+            x = layers.data(name='x', shape=[13], dtype='float32')
+            y_predict = layers.fc(input=x, size=1, act=None)
+            y = layers.data(name='y', shape=[1], dtype='float32')
+            cost = layers.square_error_cost(input=y_predict, label=y)
+            avg_cost = layers.mean(x=cost)
+            opt = optimizer.SGD(learning_rate=0.001)
+            opt = opt.minimize(avg_cost)
+
+        self.program = program
+
+    def test_control_flow_graph(self):
+        print("before optimization")
+        print(str(self.program))
+        result_program = memory_optimize(self.program)
+        print("after optimization")
+        print(str(result_program))
+
+
+if __name__ == "__main__":
+    unittest.main()