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173 lines
6.7 KiB
173 lines
6.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import defaultdict
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import framework
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from framework import Program, default_main_program, Parameter, Variable
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import backward
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from backward import _rename_arg_
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from . import core
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dtype_to_size = {
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core.DataType.FP16: 2,
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core.DataType.FP32: 4,
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core.DataType.FP64: 8,
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core.DataType.INT16: 2,
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core.DataType.INT32: 4,
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core.DataType.INT64: 8,
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core.DataType.BOOL: 1
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}
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class ControlFlowGraph(object):
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def __init__(self, Program):
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self._program = Program
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self._succesors = defaultdict(set)
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self._presucessors = defaultdict(set)
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self._uses = defaultdict(set)
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self._defs = defaultdict(set)
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self._live_in = defaultdict(set)
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self._live_out = defaultdict(set)
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def _add_connections(self, connections):
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for node1, node2 in connections:
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self._add(node1, node2)
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def _add(self, node1, node2):
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self._succesors[node1].add(node2)
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self._presucessors[node2].add(node1)
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def _build_graph(self):
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program_desc = self._program.get_desc()
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block_size = program_desc.num_blocks()
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# TODO(qijun) handle Program with if/while operators
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self.global_block_desc = program_desc.block(0)
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self.op_size = self.global_block_desc.op_size()
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op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
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self._add_connections(op_node_connections)
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self.ops = [self.global_block_desc.op(i) for i in range(self.op_size)]
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for i in range(self.op_size):
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self._uses[i].update(self.ops[i].input_arg_names())
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self._defs[i].update(self.ops[i].output_arg_names())
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def _update_graph(self, old_name, new_name, begin_idx=0):
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for i in range(begin_idx, self.op_size):
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if old_name in self._uses[i]:
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self._uses[i].remove(old_name)
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self._uses[i].add(new_name)
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if old_name in self._defs[i]:
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self._defs[i].remove(old_name)
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self._defs[i].add(new_name)
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if old_name in self._live_in[i]:
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self._live_in[i].remove(old_name)
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self._live_out[i].add(new_name)
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if old_name in self._live_out[i]:
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self._live_out[i].remove(old_name)
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self._live_out[i].add(new_name)
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def _reach_fixed_point(self, live_in, live_out):
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if len(live_in) != len(self._live_in):
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return False
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if len(live_out) != len(self._live_out):
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return False
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for i in range(self.op_size):
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if live_in[i] != self._live_in[i]:
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return False
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for i in range(self.op_size):
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if live_out[i] != self._live_out[i]:
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return False
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return True
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def _dataflow_analyze(self):
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self._build_graph()
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live_in = defaultdict(set)
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live_out = defaultdict(set)
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while True:
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for i in range(self.op_size):
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live_in[i] = set(self._live_in[i])
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live_out[i] = set(self._live_out[i])
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self._live_in[i] = self._uses[i] | (
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self._live_out[i] - self._defs[i])
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for s in self._succesors[i]:
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self._live_out[i] |= self._live_in[s]
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if self._reach_fixed_point(live_in, live_out):
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break
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def _get_diff(self, a, b):
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u = a & b
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return a - u, b - u
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def memory_optimize(self):
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self._build_graph()
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self._dataflow_analyze()
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self.pool = []
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for i in range(self.op_size):
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if self.pool:
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out_pair = [(x, self.global_block_desc.var(str(x)).shape())
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for x in self._defs[i]]
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for x, x_shape in out_pair:
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if not self.global_block_desc.var(str(x)).persistable():
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for index, cache_pair in enumerate(self.pool):
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cache_var = cache_pair[0]
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cache_shape = cache_pair[1]
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if x_shape == cache_shape:
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x_dtype = self.global_block_desc.var(str(
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x)).dtype()
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cache_dtype = self.global_block_desc.var(
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str(cache_var)).dtype()
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# TODO(qijun): actually, we should compare dtype_to_size[x_dtype]
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# and dtype_to_size[cache_dtype]
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if x_dtype == cache_dtype:
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print(
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("Hit Cache !!!! cache pool index "
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"is %d, var name is %s, "
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"cached var name is %s, "
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"var shape is %s ") %
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(index, x, cache_var, str(cache_shape)))
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self.pool.pop(index)
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_rename_arg_(
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self.ops, x, cache_var, begin_idx=i)
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self._program.current_block().var(str(
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x)).desc = self.global_block_desc.var(
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str(cache_var))
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self._update_graph(
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x, cache_var, begin_idx=i)
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break
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in_diff, out_diff = self._get_diff(self._live_in[i],
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self._live_out[i])
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can_optimize = filter(
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lambda x: not self.global_block_desc.var(str(x)).persistable(),
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in_diff)
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if can_optimize:
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for var_name in can_optimize:
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self.pool.append(
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(var_name,
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self.global_block_desc.var(str(var_name)).shape()))
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def get_program(self):
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return self._program
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def memory_optimize(input_program):
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graph = ControlFlowGraph(input_program)
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graph.memory_optimize()
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result_program = graph.get_program()
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return result_program
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