Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_tensorrt_softmax
xzl
7 years ago
commit
29ad9794bb
@ -0,0 +1,35 @@
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if(NOT WITH_GPU)
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return()
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endif()
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include(ExternalProject)
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set(CUB_SOURCE_DIR ${THIRD_PARTY_PATH}/cub)
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set(CUB_INCLUDE_DIR ${CUB_SOURCE_DIR}/src/extern_cub)
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include_directories(${CUB_INCLUDE_DIR})
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ExternalProject_Add(
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extern_cub
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${EXTERNAL_PROJECT_LOG_ARGS}
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GIT_REPOSITORY "https://github.com/NVlabs/cub.git"
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GIT_TAG "v1.8.0"
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PREFIX ${CUB_SOURCE_DIR}
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UPDATE_COMMAND ""
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CONFIGURE_COMMAND ""
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BUILD_COMMAND ""
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INSTALL_COMMAND ""
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TEST_COMMAND ""
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)
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if(${CMAKE_VERSION} VERSION_LESS "3.3.0")
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set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cub_dummy.c)
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file(WRITE ${dummyfile} "const char *dummy = \"${dummyfile}\";")
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add_library(cub STATIC ${dummyfile})
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else()
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add_library(cub INTERFACE)
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endif()
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add_dependencies(cub extern_cub)
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LIST(APPEND externl_project_dependencies cub)
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@ -0,0 +1,102 @@
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# 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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"""
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This module privides a memory usage calculate function for user.
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The purpose of this API is to allow users to estimate memory usage of
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a program under a special batch size, then user can set appropriate
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batch size to fully utilize a GPU.
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This API is still under active development and may change drastically.
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"""
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from .. import core
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from ..framework import Program, Variable
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__all__ = ['memory_usage']
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dtype_to_size = {
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core.VarDesc.VarType.FP16: 2,
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core.VarDesc.VarType.FP32: 4,
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core.VarDesc.VarType.FP64: 8,
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core.VarDesc.VarType.INT16: 2,
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core.VarDesc.VarType.INT32: 4,
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core.VarDesc.VarType.INT64: 8,
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core.VarDesc.VarType.BOOL: 1,
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core.VarDesc.VarType.UINT8: 1,
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}
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DEBUG = False
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def memory_usage(program, batch_size):
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"""
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Get the estimate memory usage of program with input batch size.
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Args:
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program(Program): The current Program.
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batch_size(int): The current input data batch_size.
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Returns:
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min_total_memory(float): the estimate memory usage lower bound.
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max_total_memory(float): the estimate memory usage upper bound.
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unit_str(string): the unit of estimate usage result.
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Examples:
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>>> import paddle.fluid as fluid
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>>> lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
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fluid.default_main_program(), batch_size=10)
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>>> print "memory usage is about %.3f - %.3f %s" % \
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(lower_usage, upper_usage, unit)
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"""
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# Parameters check
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if not isinstance(program, Program):
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raise TypeError(
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"Calculating Memory Usage requires Program as its Parameter."
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"But you passed in %s" % (type(prgram)))
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if batch_size <= 0:
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raise ValueError("The batch size need to be positive.")
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# Get the var_name list of first block and calculate
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total_memory = 0.0
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for var in program.global_block().vars.itervalues():
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data_count = 1
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for x in var.shape:
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if x == -1:
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data_count *= batch_size
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else:
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data_count *= x
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var_memory = data_count * dtype_to_size[var.dtype]
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if DEBUG:
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print "%s memory usage: %d" % (var.name, var_memory)
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total_memory += var_memory
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if DEBUG:
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print "total memory usage: %.2f" % (total_memory)
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# Convert appropriate unit
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unit_str = "B"
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if total_memory > 1024:
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total_memory /= 1024
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unit_str = "KB"
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if total_memory > 1024:
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total_memory /= 1024
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unit_str = "MB"
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# Append extra memory consumption (5% - 10%)
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min_total_memory = total_memory * 1.05
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max_total_memory = total_memory * 1.1
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return min_total_memory, max_total_memory, unit_str
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@ -0,0 +1,69 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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import contextlib
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import unittest
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def train_simulator(test_batch_size=10):
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if test_batch_size <= 0:
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raise ValueError("batch_size should be a positive integeral value, "
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"but got batch_size={}".format(test_batch_size))
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(cost)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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# Calculate memory usage in current network config
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lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
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fluid.default_main_program(), batch_size=test_batch_size)
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print("memory usage is about %.3f - %.3f %s" %
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(lower_usage, upper_usage, unit))
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class TestMemoryUsage(unittest.TestCase):
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def test_with_unit_B(self):
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with self.program_scope_guard():
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train_simulator()
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def test_with_unit_KB(self):
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with self.program_scope_guard():
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train_simulator(test_batch_size=1000)
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def test_with_unit_MB(self):
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with self.program_scope_guard():
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train_simulator(test_batch_size=100000)
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@contextlib.contextmanager
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def program_scope_guard(self):
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prog = fluid.Program()
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startup_prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog, startup_prog):
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yield
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if __name__ == '__main__':
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unittest.main()
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