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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 collections import defaultdict
import framework
from framework import Program, default_main_program, Parameter, Variable
import backward
from backward import _rename_arg_
from . import core
dtype_to_size = {
core.DataType.FP16: 2,
core.DataType.FP32: 4,
core.DataType.FP64: 8,
core.DataType.INT16: 2,
core.DataType.INT32: 4,
core.DataType.INT64: 8,
core.DataType.BOOL: 1
}
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_desc = program_desc.block(0)
self.op_size = self.global_block_desc.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_desc.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 _update_graph(self, old_name, new_name, begin_idx=0):
for i in range(begin_idx, self.op_size):
if old_name in self._uses[i]:
self._uses[i].remove(old_name)
self._uses[i].add(new_name)
if old_name in self._defs[i]:
self._defs[i].remove(old_name)
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)
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):
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_desc.var(str(x)).shape())
for x in self._defs[i]]
for x, x_shape in out_pair:
if not self.global_block_desc.var(str(x)).persistable():
for index, cache_pair in enumerate(self.pool):
cache_var = cache_pair[0]
cache_shape = cache_pair[1]
if x_shape == cache_shape:
x_dtype = self.global_block_desc.var(str(
x)).dtype()
cache_dtype = self.global_block_desc.var(
str(cache_var)).dtype()
# TODO(qijun): actually, we should compare dtype_to_size[x_dtype]
# and dtype_to_size[cache_dtype]
if x_dtype == cache_dtype:
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._program.current_block().var(str(
x)).desc = self.global_block_desc.var(
str(cache_var))
self._update_graph(
x, cache_var, begin_idx=i)
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_desc.var(str(x)).persistable(),
in_diff)
if can_optimize:
for var_name in can_optimize:
self.pool.append(
(var_name,
self.global_block_desc.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