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PaddleDetection
提交 ef60a654 编写于 作者: D dzhwinter
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"add test"

上级 0c1a5d87
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Showing with 65 addition and 75 deletion
python/paddle/fluid/tests/unittests/test_memory_optimization_transpiler.py
浏览文件 @ ef60a654
......@@ -66,6 +66,31 @@ class TestMemoryTranspiler2(unittest.TestCase):
print("after optimization")
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
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
浏览文件 @ ef60a654
......@@ -47,6 +47,7 @@ PRINT_LOG = False
class ControlFlowGraph(object):
def __init__(self, program, ops, forward_num, skip_opt):
self._program = program
self._dup_program = program.clone()
self._ops = ops
self._forward_num = forward_num
self._successors = defaultdict(set)
......@@ -56,6 +57,7 @@ class ControlFlowGraph(object):
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."""
......@@ -78,8 +80,6 @@ class ControlFlowGraph(object):
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]
# print(self._successors)
# print(self._presuccessors)
def _update_graph(self, old_name, new_name, begin_idx=0):
for i in range(begin_idx, self.op_size):
......@@ -89,50 +89,13 @@ class ControlFlowGraph(object):
if old_name in self._defs[i]:
self._defs[i].remove(old_name)
self._defs[i].add(new_name)
# for i in range(begin_idx, -1, -1):
if old_name in self._live_in[i]:
self._live_in[i].remove(old_name)
self._live_in[i].add(new_name)
# if old_name == "concat_3.tmp_0@GRAD":
# print("new_name", new_name)
# print("live_in ", i , self._live_in[i])
if old_name in self._live_out[i]:
self._live_out[i].remove(old_name)
self._live_out[i].add(new_name)
# if old_name == "concat_3.tmp_0@GRAD":
# print("live_out ", i , self._live_out[i])
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.
# counter = 0
# print(self._successors)
# while True:
# counter += 1
# 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
def _dataflow_analyze(self):
self._build_graph()
......@@ -149,6 +112,20 @@ class ControlFlowGraph(object):
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
return a - u, b - u
......@@ -238,24 +215,15 @@ class ControlFlowGraph(object):
# update skip set to meet users' demand
if skip_opt_set:
self._skip_opt.update(skip_opt_set)
self.pool = []
# self.pool = []
for i in range(self.op_size):
op = self._ops[i]
if op.type() in SUB_BLOCK_OPS:
continue
block_desc = op.block()
is_forward = i < self._forward_num
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)
# print(op.type(), i, self.pool)
# print(self._live_in[i])
if self.pool:
defs_can_optimize = [
x for x in self._defs[i]
......@@ -266,60 +234,57 @@ class ControlFlowGraph(object):
for x in defs_can_optimize
]
for x, x_shape in out_pair:
if (x, x_shape) in self.pool:
raise ValueError("x in pool")
# If x is both in uses and defs, it can not be optimized!
if x in self._uses[i]:
# print(self.pool, op.type(), cpt.to_text(x))
# raise ValueError("x in use!", cpt.to_text(x))
continue
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):
continue
raise ValueError("cache", cpt.to_text(cache_var), " Not exists!")
if x == cache_var:
raise ValueError("x : ", cpt.to_text(x), " cache : ", cpt.to_text(cache_var), " is same var!")
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:
continue
self.pool.pop(index)
if x == cache_var:
break
if PRINT_LOG:
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 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)
self._program.block(block_desc.id).var(cpt.to_text(
x)).desc = self._find_var(block_desc, cache_var,
is_forward)
if x == "concat_3.tmp_0@GRAD":
print("Update Graph", i)
self._program.block(block_desc.id)._remove_var(cpt.to_text(
x))
# if str(self._program) != str(self._dup_program):
# with open("./program_middle", "w") as f:
# f.write(str(self._program))
# f.flush()
# exit(0)
# self._program.block(block_desc.id).var(cpt.to_text(
# x)).desc = self._find_var(block_desc, cache_var,
# is_forward)
self._update_graph(x, cache_var, begin_idx=i)
break
# self._fill_pool(i, is_forward)
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)
]
keys = set([key for key,shape in self.pool])
if can_optimize:
for var_name in can_optimize:
if var_name not in keys:
self.pool.append((var_name, self._find_var(
block_desc, var_name, is_forward).shape()))
# print(op.type(), i, self.pool)
def _process_sub_block_pair(pdesc, sub_block_pair):
......
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