Merge pull request #13351 from dzhwinter/memory/stable

fix memory optimize

Author: reyoung

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

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import unittest
 
+import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.fluid.optimizer as optimizer
 from paddle.fluid.framework import Program, program_guard
@@ -67,5 +68,34 @@ def test_inplace_ops(self):
         print(str(result_program))
 
 
+class TestMemoryTranspiler3(unittest.TestCase):
+    def setUp(self):
+        program = Program()
+        with program_guard(program, startup_program=Program()):
+            word = fluid.layers.data(name='word', shape=[1], dtype='int64')
+            emb = [
+                fluid.layers.embedding(
+                    word, size=[65536, 256], param_attr='emb') for _ in range(6)
+            ]
+
+            left = emb.pop(0)
+            while len(emb) != 0:
+                right = emb.pop(0)
+                left = fluid.layers.concat([left, right])
+            emb = fluid.layers.mean(left)
+            fluid.backward.append_backward(emb)
+        self.program = program
+
+    def test_cascade_reuse(self):
+        block = self.program.block(0)
+        # variable reuse in programdesc
+        # TODO(dzhwinter): confirm cascade strategy. disable temporialy
+        self.assertTrue("concat_4.tmp_0@GRAD" in block.vars)
+        # self.assertTrue("concat_3.tmp_0@GRAD" not in block.vars)
+        # self.assertTrue("concat_2.tmp_0@GRAD" not in block.vars)
+        # self.assertTrue("concat_1.tmp_0@GRAD" not in block.vars)
+        # self.assertTrue("concat_0.tmp_0@GRAD" not in block.vars)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -56,6 +56,7 @@ def __init__(self, program, ops, forward_num, skip_opt):
         self._live_in = defaultdict(set)
         self._live_out = defaultdict(set)
         self._skip_opt = skip_opt
+        self.pool = []
 
     def _add_connections(self, connections):
         """Populates _successors and _presuccessors for two neighbor nodes."""
@@ -77,6 +78,7 @@ def _build_graph(self):
         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())
+            self._live_in[i] = self._uses[i]
 
     def _update_graph(self, old_name, new_name, begin_idx=0):
         for i in range(begin_idx, self.op_size):
@@ -88,39 +90,39 @@ def _update_graph(self, old_name, new_name, begin_idx=0):
                 self._defs[i].add(new_name)
             if old_name in self._live_in[i]:
                 self._live_in[i].remove(old_name)
-                self._live_out[i].add(new_name)
+                self._live_in[i].add(new_name)
             if old_name in self._live_out[i]:
                 self._live_out[i].remove(old_name)
                 self._live_out[i].add(new_name)
 
-    def _reach_fixed_point(self, live_in, live_out):
-        """Check if the liveness set has stablized."""
-        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] or
-                    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)
-        # Repeatedly apply liveness updates until the algorithm stablize
-        # on a complete set live input vars and live output vars.
-        while True:
-            for i in reversed(list(range(self.op_size))):
-                live_in[i] = set(self._live_in[i])
-                live_out[i] = set(self._live_out[i])
-                for s in self._successors[i]:
-                    self._live_out[i] |= self._live_in[s]
-                self._live_in[i] = self._uses[i] | (
-                    self._live_out[i] - self._defs[i])
-            if self._reach_fixed_point(live_in, live_out):
-                break
+        worklist = list(range(len(self._ops) - 1, -1, -1))
+        while worklist:
+            i = worklist.pop(0)
+            live_in[i] = set(self._live_in[i])
+            for s in self._successors[i]:
+                self._live_out[i] |= self._live_in[s]
+            self._live_in[i] = self._uses[i] | (
+                self._live_out[i] - self._defs[i])
+            if live_in[i] != self._live_in[i]:
+                for d in self._presuccessors[i]:
+                    worklist.append(d)
+
+    def _fill_pool(self, i, is_forward):
+        block_desc = self._ops[i].block()
+        in_diff, _ = self._get_diff(self._live_in[i], self._live_out[i])
+        can_optimize = [
+            x for x in in_diff
+            if self._check_var_validity(block_desc, x, is_forward)
+        ]
+        if can_optimize:
+            for var_name in can_optimize:
+                cache = (var_name, self._find_var(block_desc, var_name,
+                                                  is_forward).shape())
+                if cache not in self.pool:
+                    self.pool.append(cache)
 
     def _get_diff(self, a, b):
         u = a & b
@@ -211,7 +213,6 @@ def compare_shape(x_shape, cache_shape, opt_level):
         # update skip set to meet users' demand
         if skip_opt_set:
             self._skip_opt.update(skip_opt_set)
-        self.pool = []
         for i in range(self.op_size):
             op = self._ops[i]
             if op.type() in SUB_BLOCK_OPS:
@@ -234,16 +235,24 @@ def compare_shape(x_shape, cache_shape, opt_level):
                     for index, cache_pair in enumerate(self.pool):
                         cache_var = cache_pair[0]
                         cache_shape = cache_pair[1]
-                        if not compare_shape(x_shape, cache_shape, level):
-                            continue
-
                         if not self._has_var(block_desc, cache_var, is_forward):
+                            if PRINT_LOG:
+                                print("cache %s not exists!" %
+                                      (cpt.to_text(cache_var)))
                             continue
+                        if x == cache_var:
+                            if PRINT_LOG:
+                                print("x : ", cpt.to_text(x), " cache : ",
+                                      cpt.to_text(cache_var), " is same var!")
+                            break
 
                         x_dtype = self._find_var(block_desc, x,
                                                  is_forward).dtype()
                         cache_dtype = self._find_var(block_desc, cache_var,
                                                      is_forward).dtype()
+
+                        if not compare_shape(x_shape, cache_shape, level):
+                            continue
                         # TODO(qijun): actually, we should compare
                         # dtype_to_size[x_dtype] and dtype_to_size[cache_dtype]
                         if x_dtype != cache_dtype:
@@ -256,8 +265,6 @@ def compare_shape(x_shape, cache_shape, opt_level):
                                    "var shape is %s ") % (index, x, cache_var,
                                                           str(cache_shape)))
                         self.pool.pop(index)
-                        if x == cache_var:
-                            break
                         # Rename the var to the cache var already with
                         # memory allocated in order to reuse the memory.
                         _rename_arg_(self._ops, x, cache_var, begin_idx=i)
@@ -266,16 +273,7 @@ def compare_shape(x_shape, cache_shape, opt_level):
                                                       is_forward)
                         self._update_graph(x, cache_var, begin_idx=i)
                         break
-
-            in_diff, _ = self._get_diff(self._live_in[i], self._live_out[i])
-            can_optimize = [
-                x for x in in_diff
-                if self._check_var_validity(block_desc, x, is_forward)
-            ]
-            if can_optimize:
-                for var_name in can_optimize:
-                    self.pool.append((var_name, self._find_var(
-                        block_desc, var_name, is_forward).shape()))
+            self._fill_pool(i, is_forward)
 
 
 def _process_sub_block_pair(pdesc, sub_block_pair):
@@ -383,10 +381,13 @@ def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
 
       Note: it doesn't not support subblock nested in subblock.
 
-    :param input_program: Input Program
-    :param print_log: whether to print debug log.
-    :param level: If level=0, reuse if the shape is completely equal, o
-    :return:
+    Args:
+        input_program(str): Input Program
+        skip_opt_set(set): vars wil be skipped in memory optimze
+        print_log(bool): whether to print debug log.
+        level(int): If level=0, reuse if the shape is completely equal, o
+    Returns:
+        None
     """
     if level != 0 and level != 1:
         raise ValueError("only support opt_level 0 or 1.")
@@ -407,6 +408,9 @@ def release_memory(input_program, skip_opt_set=None):
 
     Args:
         input_program(Program): The program will be inserted :code:`delete_op`.
+        skip_opt_set(set): vars wil be skipped in memory optimze
+    Returns:
+        None
     """
     cfgs = _get_cfgs(input_program)
     for cfg in cfgs: