未验证 提交 b2435a3a 编写于 作者: C Chen Weihang 提交者: GitHub

Merge pull request #12374 from chenwhql/py_calc_memory

Add memory usage estimate API 
...@@ -336,6 +336,7 @@ paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=Non ...@@ -336,6 +336,7 @@ paddle.fluid.contrib.BeamSearchDecoder.decode ArgSpec(args=['self'], varargs=Non
paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) paddle.fluid.contrib.BeamSearchDecoder.early_stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False)) paddle.fluid.contrib.BeamSearchDecoder.read_array ArgSpec(args=['self', 'init', 'is_ids', 'is_scores'], varargs=None, keywords=None, defaults=(False, False))
paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None) paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.transpiler.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None) paddle.fluid.transpiler.DistributeTranspiler.create_splited_vars ArgSpec(args=['self', 'source_var', 'block', 'tag'], varargs=None, keywords=None, defaults=None)
paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None) paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
......
...@@ -14,5 +14,7 @@ ...@@ -14,5 +14,7 @@
import decoder import decoder
from decoder import * from decoder import *
import memory_usage_calc
from memory_usage_calc import *
__all__ = decoder.__all__ __all__ = decoder.__all__ + memory_usage_calc.__all__
# 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.
"""
This module privides a memory usage calculate function for user.
The purpose of this API is to allow users to estimate memory usage of
a program under a special batch size, then user can set appropriate
batch size to fully utilize a GPU.
This API is still under active development and may change drastically.
"""
from .. import core
from ..framework import Program, Variable
__all__ = ['memory_usage']
dtype_to_size = {
core.VarDesc.VarType.FP16: 2,
core.VarDesc.VarType.FP32: 4,
core.VarDesc.VarType.FP64: 8,
core.VarDesc.VarType.INT16: 2,
core.VarDesc.VarType.INT32: 4,
core.VarDesc.VarType.INT64: 8,
core.VarDesc.VarType.BOOL: 1,
core.VarDesc.VarType.UINT8: 1,
}
DEBUG = False
def memory_usage(program, batch_size):
"""
Get the estimate memory usage of program with input batch size.
Args:
program(Program): The current Program.
batch_size(int): The current input data batch_size.
Returns:
min_total_memory(float): the estimate memory usage lower bound.
max_total_memory(float): the estimate memory usage upper bound.
unit_str(string): the unit of estimate usage result.
Examples:
>>> import paddle.fluid as fluid
>>> lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
fluid.default_main_program(), batch_size=10)
>>> print "memory usage is about %.3f - %.3f %s" % \
(lower_usage, upper_usage, unit)
"""
# Parameters check
if not isinstance(program, Program):
raise TypeError(
"Calculating Memory Usage requires Program as its Parameter."
"But you passed in %s" % (type(prgram)))
if batch_size <= 0:
raise ValueError("The batch size need to be positive.")
# Get the var_name list of first block and calculate
total_memory = 0.0
for var in program.global_block().vars.itervalues():
data_count = 1
for x in var.shape:
if x == -1:
data_count *= batch_size
else:
data_count *= x
var_memory = data_count * dtype_to_size[var.dtype]
if DEBUG:
print "%s memory usage: %d" % (var.name, var_memory)
total_memory += var_memory
if DEBUG:
print "total memory usage: %.2f" % (total_memory)
# Convert appropriate unit
unit_str = "B"
if total_memory > 1024:
total_memory /= 1024
unit_str = "KB"
if total_memory > 1024:
total_memory /= 1024
unit_str = "MB"
# Append extra memory consumption (5% - 10%)
min_total_memory = total_memory * 1.05
max_total_memory = total_memory * 1.1
return min_total_memory, max_total_memory, unit_str
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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 __future__ import print_function
import paddle
import paddle.fluid as fluid
import contextlib
import unittest
def train_simulator(test_batch_size=10):
if test_batch_size <= 0:
raise ValueError("batch_size should be a positive integeral value, "
"but got batch_size={}".format(test_batch_size))
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
# Calculate memory usage in current network config
lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
fluid.default_main_program(), batch_size=test_batch_size)
print("memory usage is about %.3f - %.3f %s" %
(lower_usage, upper_usage, unit))
class TestMemoryUsage(unittest.TestCase):
def test_with_unit_B(self):
with self.program_scope_guard():
train_simulator()
def test_with_unit_KB(self):
with self.program_scope_guard():
train_simulator(test_batch_size=1000)
def test_with_unit_MB(self):
with self.program_scope_guard():
train_simulator(test_batch_size=100000)
@contextlib.contextmanager
def program_scope_guard(self):
prog = fluid.Program()
startup_prog = fluid.Program()
scope = fluid.core.Scope()
with fluid.scope_guard(scope):
with fluid.program_guard(prog, startup_prog):
yield
if __name__ == '__main__':
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册