Source code for paddle.fluid.compiler

#   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.

import logging
import multiprocessing
import os
import six
import sys
from .. import compat as cpt
from . import framework
from .framework import cuda_places, cpu_places

from . import core

__all__ = ['CompiledProgram', 'ExecutionStrategy', 'BuildStrategy']

ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
InferNativeConfig = core.NativeConfig
InferAnalysisConfig = core.AnalysisConfig


def _place_obj(place):
    p = core.Place()
    p.set_place(place)
    return p


def _is_pserver_mode(main_program):
    main = main_program if main_program \
        else framework.default_main_program()
    for op in main.global_block().ops:
        if op.type in ["send", "recv"]:
            return True
    return False


[docs]class CompiledProgram(object): """ Compiles to Graph for execution. 1. Users first create the program with layers. 2. Optionally, users use CompiledProgram to optimize the program before run. 3. The original program or CompiledProgram is run by executor. The CompiledProgram is used to transform a program for various optimizations, for example. * Pre-compute some logic once so that each run is faster. * Transform the program so that it can run in multiple devices. * Transform the program for optimized inference or distributed training. **Note that: this part is not finished.** Example: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.compiler as compiler import numpy import os place = fluid.CUDAPlace(0) # fluid.CPUPlace() exe = fluid.Executor(place) data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) fluid.default_startup_program().random_seed=1 exe.run(fluid.default_startup_program()) compiled_prog = compiler.CompiledProgram( fluid.default_main_program()) x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = exe.run(compiled_prog, feed={"X": x}, fetch_list=[loss.name]) Args: program_or_graph (Graph|Program): If it's Program, it will be first lowered to a graph for further optimizations. If it's a graph (potentially optimized before), it will be directly used for further optimizations. Note: graph is only supported when compiled with with_data_parallel option. """ def __init__(self, program_or_graph): if isinstance(program_or_graph, core.Graph): self._graph = program_or_graph # don't not create a new program here. self._program = None elif isinstance(program_or_graph, framework.Program): self._graph = core.Graph(program_or_graph.desc) self._program = program_or_graph else: raise ValueError("Wrong program_to_graph type: %s" % type(program_or_graph)) self._scope = None self._place = None self._executor = None self._compiled = False self._is_data_parallel = False self._is_inference = False
[docs] def with_data_parallel(self, loss_name=None, build_strategy=None, exec_strategy=None, share_vars_from=None, places=None): """Configs the program to run in data parallel way. Example: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.compiler as compiler import numpy import os use_cuda = True place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() # NOTE: If you use CPU to run the program, you need # to specify the CPU_NUM, otherwise, fluid will use # all the number of the logic core as the CPU_NUM, # in that case, the batch size of the input should be # greater than CPU_NUM, if not, the process will be # failed by an exception. if not use_cuda: os.environ['CPU_NUM'] = str(2) exe = fluid.Executor(place) data = fluid.layers.data(name='X', shape=[1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) fluid.default_startup_program().random_seed=1 exe.run(fluid.default_startup_program()) compiled_prog = compiler.CompiledProgram( fluid.default_main_program()).with_data_parallel( loss_name=loss.name) x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = exe.run(compiled_prog, feed={"X": x}, fetch_list=[loss.name]) Args: loss_name (str): The loss name must set in training. Default None. build_strategy(BuildStrategy): build_strategy is used to build the graph so it can run on multiple devices/cores with optimized topology. For more information, please refer to fluid.BuildStrategy. Default None. exec_strategy(ExecutionStrategy): exec_strategy is used to to select the a way to execute the graph, for example how many threads are used, how many iterations to clean up the temp variables. For more information, please refer to fluid.ExecutionStrategy. Default None. share_vars_from(CompiledProgram): If provided, this CompiledProgram will share variables from `share_vars_from`. `share_vars_from` must be run by the executor before this CompiledProgram so that vars are ready. places(list(CUDAPlace)|list(CPUPlace)|None): If provided, only compile program in the given places. Otherwise, the places used when compiled is determined by the Executor, and the places used are controlled by environment variables: FLAGS_selected_gpus or CUDA_VISIBLE_DEVICES if using GPU; or CPU_NUM if using CPU. For example, if you want to run on GPU 0 and 1, set places=[fluid.CUDAPlace(0), fluid.CUDAPlace(1)]. If you want to run on 2 CPU cores, set places=[fluid.CPUPlace()]*2. Returns: self """ assert not self._is_data_parallel, "Already compiled with parallel." assert not self._is_inference, "Cannot compile both data parallel and inference" self._is_data_parallel = True self._build_strategy = build_strategy self._exec_strategy = exec_strategy self._loss_name = loss_name self._share_vars_from = share_vars_from if self._exec_strategy is None: self._exec_strategy = ExecutionStrategy() if self._build_strategy is None: self._build_strategy = BuildStrategy() if places is not None: if not isinstance(places, (list, tuple)): places = [places] self._places = places else: self._places = None self._build_strategy.is_distribution = _is_pserver_mode(self._program) # FIXME(dzhwinter): enable_inplace should be after memory_optimize # if turn on python memory optimize, turn off the inplace_pass. # memory_optimize and enable_inplace default are True, but we can disable them on purpose if self._program: if self._program._is_mem_optimized: self._build_strategy.memory_optimize = False self._build_strategy.enable_inplace = False elif not self._build_strategy.memory_optimize or not self._build_strategy.enable_inplace: # remind the user to try our memmory optimize strategy logging.warn(""" You can try our memory optimize feature to save your memory usage: # create a build_strategy variable to set memory optimize option build_strategy = compiler.BuildStrategy() build_strategy.enable_inplace = True build_strategy.memory_optimize = True # pass the build_strategy to with_data_parallel API compiled_prog = compiler.CompiledProgram(main).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy) !!! Memory optimize is our experimental feature !!! some variables may be removed/reused internal to save memory usage, in order to fetch the right value of the fetch_list, please set the persistable property to true for each variable in fetch_list # Sample conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None) # if you need to fetch conv1, then: conv1.persistable = True """) return self
[docs] def with_inference_optimize(self, config): """ Add inference optimize Args: config: instance of `NativeConfig` or `AnalysisConfig` to create predictor Returns: self """ assert not self._is_data_parallel, "Cannot compile both data parallel and inference" assert not self._is_inference, "Already compiled with inference" assert any([ isinstance(config, InferNativeConfig), isinstance(config, InferAnalysisConfig) ]) self._is_inference = True self._infer_config = config return self
def _with_distributed(self): raise NotImplementedError() def _compile_data_parallel(self, use_cuda=False, scope=None): if self._share_vars_from: if scope: sys.stderr.write("share_vars_from is set, scope is ignored.\n") if not self._share_vars_from._is_data_parallel: raise ValueError("share_vars_from is not data parallel. Cannot " "share vars from it.") if self._share_vars_from._executor is None: raise ValueError( "share_vars_from is not compiled and run, so there is no " "var to share.") self._local_scopes = self._share_vars_from._executor.local_scopes() # drop the local_exe_scopes of the previous parallel_executor self._share_vars_from._executor.drop_local_exe_scopes() else: assert scope is not None, "" self._local_scopes = [] self._exec_strategy.use_cuda = use_cuda has_set_place = (self._places is not None) if has_set_place: for p in self._places: assert p._type() == self._place._type(), \ "Place type not match. You may set the wrong type of places" else: self._places = cuda_places( ) if self._exec_strategy.use_cuda else cpu_places() assert self._places, "no place for execution" if self._exec_strategy.num_threads == 0: if self._exec_strategy.use_cuda: # Experiments on se-resnext shows that too many threads hurt # performance. Worth tunning for other models in the future. self._exec_strategy.num_threads = len(self._places) * 4 else: self._exec_strategy.num_threads = len(self._places) * 2 # TODO(wuyi): trainer endpoings should be passed in through # build_strategy, not program.xxx. # TODO(gongwb): let user to set them once. if self._program and self._build_strategy.num_trainers > 1 and \ self._program._trainers_endpoints: tps = self._program._trainers_endpoints assert self._build_strategy.num_trainers == len( tps), "num_trainers == len(end_points)" self._build_strategy.trainers_endpoints = tps if self._program: self._build_strategy.nccl_comm_num = self._program._nccl_comm_num self._build_strategy.use_hierarchical_allreduce_ = self._program._use_hierarchical_allreduce self._build_strategy.hierarchical_allreduce_inter_nranks_ = self._program._hierarchical_allreduce_inter_nranks self._build_strategy.hierarchical_allreduce_exter_nranks_ = self._program._hierarchical_allreduce_exter_nranks if self._build_strategy.sync_batch_norm: self._build_strategy.enable_sequential_execution = True self._persistable_vars = [] for node in self._graph.nodes(): if node.is_var() and node.var() is not None and node.var().persistable() and \ node.var().type() != core.VarDesc.VarType.RAW: self._persistable_vars.append(cpt.to_text(node.name())) places = list(map(_place_obj, self._places)) # ParallelExecutor would broadcast all the parameters during initializing. # The parameters of each process should be in the same ordered for the data-parallelism # distributed training to keep the broadcast correct. self._persistable_vars = list(set(self._persistable_vars)) self._persistable_vars.sort() return core.ParallelExecutor( places, self._persistable_vars, cpt.to_text(self._loss_name) if self._loss_name else six.u(''), self._scope, self._local_scopes, self._exec_strategy, self._build_strategy, self._graph) def _compile_inference(self): return core.create_paddle_predictor(self._infer_config) def _compile(self, scope, place): """Compile the program based on the configs. Args: scope: The variables (resources) that are associated with this compiled program. place: The location that the compiled program will be run on. Returns: self """ if self._compiled: if scope and self._scope != scope: raise ValueError("Cannot compile with different scope") if place and not self._place._equals(place): raise ValueError("Cannot compile with different place") return self self._compiled = True self._scope = scope self._place = place if self._is_data_parallel: self._executor = self._compile_data_parallel( use_cuda=isinstance(self._place, core.CUDAPlace), scope=self._scope) elif self._is_inference: self._executor = self._compile_inference() else: p = _place_obj(self._place) self._executor = core.Executor(p) return self