Source code for paddle.fluid.executor

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from __future__ import print_function

import os
import multiprocessing
import numpy as np
from .wrapped_decorator import signature_safe_contextmanager
import six
from .framework import Program, default_main_program, Variable
from . import core
from . import compiler
from .. import compat as cpt
from .trainer_factory import TrainerFactory

__all__ = ['Executor', 'global_scope', 'scope_guard']

g_scope = core.Scope()
InferNativeConfig = core.NativeConfig
InferAnalysisConfig = core.AnalysisConfig


[docs]def global_scope(): """ Get the global/default scope instance. There are a lot of APIs use :code:`global_scope` as its default value, e.g., :code:`Executor.run` Returns: Scope: The global/default scope instance. """ return g_scope
def _switch_scope(scope): global g_scope ex = g_scope g_scope = scope return ex @signature_safe_contextmanager
[docs]def scope_guard(scope): """ Change the global/default scope instance by Python `with` statement. All variable in runtime will assigned to the new scope. Examples: >>> import paddle.fluid as fluid >>> new_scope = fluid.Scope() >>> with fluid.scope_guard(new_scope): >>> ... Args: scope: The new global/default scope. """ ex = _switch_scope(scope) yield _switch_scope(ex)
def as_numpy(tensor): """ Convert a Tensor to a numpy.ndarray, its only support Tensor without LoD information. For higher dimensional sequence data, please use LoDTensor directly. Examples: >>> import paddle.fluid as fluid >>> outs = executor.run(...) >>> np_outs = map(lambda x: as_numpy(x), outs) >>> ... Args: tensor(Variable): a instance of Tensor Returns: numpy.ndarray """ if isinstance(tensor, core.LoDTensorArray): return [as_numpy(t) for t in tensor] if isinstance(tensor, list): return [as_numpy(t) for t in tensor] assert isinstance(tensor, core.LoDTensor) lod = tensor.lod() if len(lod) > 0: raise RuntimeError("Some of your fetched tensors hold LoD information. \ They can not be completely cast to Python ndarray. \ Please set the parameter 'return_numpy' as 'False' to \ return LoDTensor itself directly.") return np.array(tensor) def has_feed_operators(block, feed_targets, feed_holder_name): """ Check whether the block already has feed operators. Return false if the block does not have any feed operators. If some feed operators have been prepended to the block, check that the info contained in these feed operators matches the feed_targets and feed_holder_name. Raise exception when any mismatch is found. Return true when the block has feed operators with matching info. Args: block: a block instance (typically global block of a program) feed_targets: a dictionary of {feed_target_name: feed_target_data} feed_holder_name: the name of the variable that holds the data of all feed targets. The type of this feed_holder variable is FEED_MINIBATCH, which is essentially vector<LoDTensor>. Returns: A boolean value that indicates whether a block has feed operators that match the info contained in feed_targets and feed_holder_name. """ feed_count = 0 for op in block.ops: if op.desc.type() == 'feed': feed_count += 1 assert op.desc.input('X')[0] == feed_holder_name feed_target_name = op.desc.output('Out')[0] if feed_target_name not in feed_targets: raise Exception("'feed_targets' does not have {} variable". format(feed_target_name)) else: break if feed_count > 0 and feed_count != len(feed_targets): raise Exception( "Feed operators in program desc do not match 'feed_targets'") return feed_count > 0 def has_fetch_operators(block, fetch_targets, fetch_holder_name): """ Check whether the block already has fetch operators. Return false if the block does not have any fetch operators. If some fetch operators have been appended to the block, check that the info contained in these fetch operators matches the fetch_targets and fetch_holder_name. Raise exception when any mismatch is found. Return true when the block has fetch operators with matching info. Args: block: a block instance (typically global block of a program) fetch_targets: a dictionary of {fetch_target_name: fetch_target_data} fetch_holder_name: the name of the variable that holds the data of all fetch targets. The type of this fetch_holder variable is FETCH_LIST, which is essentially vector<LoDTensor>. Return: A boolean value that indicates whether a block has fetch operators that match the info contained in fetch_targets and fetch_holder_name. """ fetch_count = 0 for op in block.ops: if op.desc.type() == 'fetch': fetch_count += 1 assert op.desc.output('Out')[0] == fetch_holder_name fetch_target_name = op.desc.input('X')[0] if fetch_target_name not in [ var.desc.name() for var in fetch_targets ]: raise Exception("'fetch_targets' does not have {} variable". format(fetch_target_name)) idx = op.desc.attr('col') assert fetch_target_name == fetch_targets[idx].desc.name() if fetch_count > 0 and fetch_count != len(fetch_targets): raise Exception( "Fetch operators in program desc do not match 'fetch_targets'") return fetch_count > 0 def _fetch_var(name, scope=None, return_numpy=True): """ Fetch the value of the variable with the given name from the given scope. Args: name(str): name of the variable. Typically, only persistable variables can be found in the scope used for running the program. scope(core.Scope|None): scope object. It should be the scope where you pass to Executor.run() when running your program. If None, global_scope() will be used. Default None. return_numpy(bool): whether convert the tensor to numpy.ndarray. Default True. Returns: LodTensor|numpy.ndarray """ assert isinstance(name, str) if scope is None: scope = global_scope() assert isinstance(scope, core._Scope) var = scope.find_var(name) assert var is not None, ( "Cannot find " + name + " in scope. Perhaps you need to make the" " variable persistable by using var.persistable = True in your" " program.") tensor = var.get_tensor() if return_numpy: tensor = as_numpy(tensor) return tensor def _to_name_str(var): if isinstance(var, Variable): return var.desc.name() elif isinstance(var, str): return var elif isinstance(var, six.string_types): return str(var) else: raise TypeError(str(var) + " should be Variable or str") def _get_program_cache_key(feed, fetch_list): feed_var_names = list(feed.keys()) fetch_var_names = list(map(_to_name_str, fetch_list)) return str(feed_var_names + fetch_var_names) def _as_lodtensor(data, place): """ Convert numpy.ndarray to Tensor, its only support Tensor without LoD information. For higher dimensional sequence data, please use LoDTensor directly. Examples: >>> import paddle.fluid as fluid >>> place = fluid.CPUPlace() >>> exe = fluid.executor(place) >>> data = np.array(size=(100, 200, 300)) >>> np_outs = map(lambda x: fluid.executor._as_lodtensor(x, place), data) >>> ... Args: data(numpy.ndarray): a instance of array Returns: LoDTensor """ if isinstance(data, list): raise RuntimeError("Some of your feed data hold LoD information. \ They can not be completely cast from a list of Python \ ndarray to LoDTensor. Please convert data to LoDTensor \ directly before feeding the data.\ ") # single tensor case tensor = core.LoDTensor() tensor.set(data, place) return tensor
[docs]class Executor(object): """ An Executor in Python, supports single/multiple-GPU running, and single/multiple-CPU running. Python executor takes a program, adds feed operators and fetch operators to this program according to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides the variables(or names) that user wants to get after program runs. Note: the executor will run all operators in the program but not only the operators dependent by the fetch_list. It stores the global variables into the global scope, and creates a local scope for the temporary variables. The contents in local scope may be discarded after every minibatch forward/backward finished. But the global scope variables will be persistent through different runs. Example: .. code-block:: python # First create the Executor. place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) # Run the startup program once and only once. # Not need to optimize/compile the startup program. exe.run(fluid.default_startup_program()) # Run the main program directly without compile. loss, = exe.run(fluid.default_main_program(), feed=feed_dict, fetch_list=[loss.name]) # Or, compiled the program and run. See `CompiledProgram` for more detail. compiled_prog = compiler.CompiledProgram( fluid.default_main_program()).with_data_parallel( loss_name=loss.name) loss, = exe.run(compiled_prog, feed=feed_dict, fetch_list=[loss.name]) Args: place(core.CPUPlace|core.CUDAPlace(n)): indicate the executor run on which device """ def __init__(self, place): self.place = place self.program_caches = dict() p = core.Place() p.set_place(self.place) self._default_executor = core.Executor(p) self._closed = False def _get_program_cache(self, program_cache_key): return self.program_caches.get(program_cache_key, None) def _add_program_cache(self, program_cache_key, program): self.program_caches[program_cache_key] = program def _add_feed_fetch_ops(self, program, feed, fetch_list, feed_var_name, fetch_var_name): tmp_program = program.clone() global_block = tmp_program.global_block() if feed_var_name in global_block.vars: feed_var = global_block.var(feed_var_name) else: feed_var = global_block.create_var( name=feed_var_name, type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True) if fetch_var_name in global_block.vars: fetch_var = global_block.var(fetch_var_name) else: fetch_var = global_block.create_var( name=fetch_var_name, type=core.VarDesc.VarType.FETCH_LIST, persistable=True) # prepend feed operators if not has_feed_operators(global_block, feed, feed_var_name): for i, name in enumerate(feed): out = global_block.var(name) global_block._prepend_op( type='feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, attrs={'col': i}) # append fetch_operators if not has_fetch_operators(global_block, fetch_list, fetch_var_name): for i, var in enumerate(fetch_list): assert isinstance(var, Variable) or isinstance( var, six.string_types), ( "Wrong type for fetch_list[%s]: %s" % (i, type(var))) global_block.append_op( type='fetch', inputs={'X': [var]}, outputs={'Out': [fetch_var]}, attrs={'col': i}) return tmp_program def _feed_data(self, program, feed, feed_var_name, scope): # feed var to framework for op in program.global_block().ops: if op.desc.type() == 'feed': feed_target_name = op.desc.output('Out')[0] cur_feed = feed[feed_target_name] if not isinstance(cur_feed, core.LoDTensor): cur_feed = _as_lodtensor(cur_feed, self.place) idx = op.desc.attr('col') core.set_feed_variable(scope, cur_feed, feed_var_name, idx) else: break def _fetch_data(self, fetch_list, fetch_var_name, scope): outs = [ core.get_fetch_variable(scope, fetch_var_name, i) for i in six.moves.range(len(fetch_list)) ] return outs ''' TODO(typhoonzero): Define "no longer use" meaning? Can user create a new Executor for the same program and run? TODO(panyx0718): Why ParallelExecutor doesn't have close? '''
[docs] def close(self): """ Close this executor. You can no longer use this executor after calling this method. For the distributed training, this method would free the resource on PServers related to the current Trainer. Example: >>> cpu = core.CPUPlace() >>> exe = Executor(cpu) >>> ... >>> exe.close() """ if not self._closed: self._default_executor.close() self._closed = True
def _run_parallel(self, program, scope, feed, fetch_list, fetch_var_name, return_numpy): exe = program._executor if isinstance(feed, dict): feed_tensor_dict = dict() for feed_name in feed: feed_tensor = feed[feed_name] if not isinstance(feed_tensor, core.LoDTensor): feed_tensor = core.LoDTensor() # always set to CPU place, since the tensor need to be splitted # it is fast in CPU feed_tensor.set(feed[feed_name], core.CPUPlace()) feed_tensor_dict[feed_name] = feed_tensor exe.feed_and_split_tensor_into_local_scopes(feed_tensor_dict) elif isinstance(feed, list) or isinstance(feed, tuple): if len(feed) != len(program._places): raise ValueError( "Feed a list of tensor, the list should be the same size as places" ) res = list() for i, each in enumerate(feed): if not isinstance(each, dict): raise TypeError( "Each element of feed list should be a dict") res_dict = dict() for feed_name in each: tensor = each[feed_name] if not isinstance(tensor, core.LoDTensor): tmp = core.LoDTensor() tmp.set(tensor, program._places[i]) tensor = tmp res_dict[feed_name] = tensor res.append(res_dict) exe.feed_tensors_into_local_scopes(res) fetch_var_names = list(map(_to_name_str, fetch_list)) exe.run(fetch_var_names, fetch_var_name) arr = scope.find_var(fetch_var_name).get_lod_tensor_array() if return_numpy: return as_numpy(arr) return [arr[i] for i in range(len(arr))]
[docs] def run(self, program=None, feed=None, fetch_list=None, feed_var_name='feed', fetch_var_name='fetch', scope=None, return_numpy=True, use_program_cache=False): """ Run program by this Executor. Feed data by feed map, fetch result by fetch_list. Python executor takes a program, add feed operators and fetch operators to this program according to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides the variables(or names) that user want to get after program run. Note: the executor will run all operators in the program but not only the operators dependent by the fetch_list Args: program(Program|CompiledProgram): the program that need to run, if not provided, then default_main_program (not compiled) will be used. feed(dict): feed variable map, e.g. {"image": ImageData, "label": LabelData} fetch_list(list): a list of variable or variable names that user wants to get, this method will return them according to this list. feed_var_name(str): the name for the input variable of feed Operator. fetch_var_name(str): the name for the output variable of fetch Operator. scope(Scope): the scope used to run this program, you can switch it to different scope. default is global_scope return_numpy(bool): if convert the fetched tensor to numpy use_program_cache(bool): whether to use the cached program settings across batches. Setting it be true would be faster only when (1) the program is not compiled with data parallel, and (2) program, feed variable names and fetch_list variable names do not changed compared to the last step. Returns: list(numpy.array): fetch result according to fetch_list. Examples: >>> data = fluid.layers.data(name='X', shape=[1], dtype='float32') >>> out = fluid.layers.create_tensor(dtype='float32') >>> hidden = fluid.layers.fc(input=data, size=10) >>> fluid.layers.assign(hidden,out) >>> loss = fluid.layers.mean(out) >>> adam = fluid.optimizer.Adam() >>> adam.minimize(loss) >>> cpu = core.CPUPlace() >>> exe = fluid.Executor(cpu) >>> exe.run(fluid.default_startup_program()) >>> x = numpy.random.random(size=(10, 1)).astype('float32') >>> outs = exe.run( >>> feed={'X': x}, >>> fetch_list=[loss.name]) """ if self._closed: raise RuntimeError("Attempted to use a closed Executor") if scope is None: scope = global_scope() if fetch_list is None: fetch_list = [] compiled = isinstance(program, compiler.CompiledProgram) # For backward compatibility, run directly. if not compiled: return self._run( program, self._default_executor, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name, scope=scope, return_numpy=return_numpy, use_program_cache=use_program_cache) program._compile(scope, self.place) if program._is_data_parallel: return self._run_parallel( program, scope=scope, feed=feed, fetch_list=fetch_list, fetch_var_name=fetch_var_name, return_numpy=return_numpy) elif program._is_inference: return self._run_inference(program._executor, feed) else: # TODO(panyx0718): Can compile program to optimize executor # performance. # TODO(panyx0718): executor should be able to run graph. assert program._program, "CompiledProgram is compiled from graph, can only run with_data_parallel." return self._run( program._program, self._default_executor, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name, scope=scope, return_numpy=return_numpy, use_program_cache=use_program_cache)
def _run(self, program, exe, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache): if feed is None: feed = {} elif isinstance(feed, (list, tuple)): assert len(feed) == 1, "Not compiled with data parallel" feed = feed[0] if not isinstance(feed, dict): raise TypeError( "feed requires dict as its Parameter. But you passed in %s" % (type(feed))) if program is None: program = default_main_program() if not isinstance(program, Program): raise TypeError( "Executor requires Program as its Parameter. But you passed in %s" % (type(program))) cache_key = _get_program_cache_key(feed, fetch_list) if use_program_cache: cached_program = self._get_program_cache(cache_key) if cached_program is None: cached_program = self._add_feed_fetch_ops( program=program, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name) self._add_program_cache(cache_key, cached_program) program = cached_program else: self.program_caches.pop(cache_key, None) program = self._add_feed_fetch_ops( program=program, feed=feed, fetch_list=fetch_list, feed_var_name=feed_var_name, fetch_var_name=fetch_var_name) self._feed_data(program, feed, feed_var_name, scope) exe.run(program.desc, scope, 0, True, True, fetch_var_name) outs = self._fetch_data(fetch_list, fetch_var_name, scope) if return_numpy: outs = as_numpy(outs) return outs def _run_inference(self, exe, feed): return exe.run(feed) def _dump_debug_info(self, program=None, trainer=None): with open(str(id(program)) + "_train_desc.prototxt", "w") as fout: fout.write(trainer._desc()) if program._fleet_opt: with open("fleet_desc.prototxt", "w") as fout: fout.write(str(program._fleet_opt["fleet_desc"])) def _prepare_trainer(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100): if scope is None: scope = global_scope() if fetch_list is None: fetch_list = [] if fetch_info is None: fetch_info = [] assert len(fetch_list) == len(fetch_info) compiled = isinstance(program, compiler.CompiledProgram) if not compiled: trainer = TrainerFactory()._create_trainer(program._fleet_opt) trainer._set_program(program) else: trainer = TrainerFactory()._create_trainer( program.program._fleet_opt) trainer._set_program(program.program) if thread <= 0: if dataset.thread_num <= 0: raise RuntimeError( "You should set thread num first, either in Dataset" "or in Executor.train_from_dataset") else: trainer._set_thread(dataset.thread_num) else: trainer._set_thread(thread) trainer._set_debug(debug) trainer._set_fetch_var_and_info(fetch_list, fetch_info, print_period) return scope, trainer
[docs] def infer_from_dataset(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100): """ The document of infer_from_dataset is almost the same as train_from_dataset, except that in distributed training, push gradients will be disabled in infer_from_dataset. infer_from_dataset() can be used for evaluation in multi-thread very easily. Args: program(Program|CompiledProgram): the program that needs to be run, if not provided, then default_main_program (not compiled) will be used. dataset(paddle.fluid.Dataset): dataset created outside this function, a user should provide a well-defined dataset before calling this function. Please check the document of Dataset if needed. default is None scope(Scope): the scope used to run this program, you can switch it to different scope for each run. default is global_scope thread(int): number of thread a user wants to run in this function. The actual number of thread will be min(Dataset.thread_num, thread) if thread > 0, default is 0 debug(bool): whether a user wants to run infer_from_dataset, default is False fetch_list(Variable List): fetch variable list, each variable will be printed during training, default is None fetch_info(String List): print information for each variable, default is None print_period(int): the number of mini-batches for each print, default is 100 Returns: None Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CPUPlace() exe = fluid.Executor(place) x = fluid.layers.data(name="x", type="int64") y = fluid.layers.data(name="y", type="int64") dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var([x, y]) filelist = ["dataA.txt", "dataB.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) exe.infer_from_dataset(program=fluid.default_main_program(), dataset=dataset) """ if dataset == None: raise RuntimeError("dataset is needed and should be initialized") if not isinstance(self.place, core.CPUPlace): raise RuntimeError("infer_from_dataset is verified on CPUPlace" "We will open CUDAPlace in the future") scope, trainer = self._prepare_trainer( program=program, dataset=dataset, scope=scope, thread=thread, debug=debug, fetch_list=fetch_list, fetch_info=fetch_info, print_period=print_period) trainer._set_infer(True) trainer._gen_trainer_desc() dataset._prepare_to_run() if debug: self._dump_debug_info(program=program, trainer=trainer) self._default_executor.run_from_dataset(program.desc, scope, dataset.dataset, trainer._desc()) return None
[docs] def train_from_dataset(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100): """ Train from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset. Given a program, either a program or compiled program, train_from_dataset will consume all data samples in dataset. Input scope can be given by users. By default, scope is global_scope(). The total number of thread run in training is `thread`. Thread number used in training will be minimum value of threadnum in Dataset and the value of thread in this interface. Debug can be set so that executor will display Run-Time for all operators and the throughputs of current training task. Note: train_from_dataset will destroy all resources created within executor for each run. Args: program(Program|CompiledProgram): the program that needs to be run, if not provided, then default_main_program (not compiled) will be used. dataset(paddle.fluid.Dataset): dataset created outside this function, a user should provide a well-defined dataset before calling this function. Please check the document of Dataset if needed. scope(Scope): the scope used to run this program, you can switch it to different scope for each run. default is global_scope thread(int): number of thread a user wants to run in this function. The actual number of thread will be min(Dataset.thread_num, thread) debug(bool): whether a user wants to run train_from_dataset fetch_list(Variable List): fetch variable list, each variable will be printed during training fetch_info(String List): print information for each variable print_period(int): the number of mini-batches for each print Returns: None Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CPUPlace() exe = fluid.Executor(place) x = fluid.layers.data(name="x", type="int64") y = fluid.layers.data(name="y", type="int64") dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var([x, y]) dataset.set_thread(2) filelist = ["dataA.txt", "dataB.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) exe.train_from_dataset(program=fluid.default_main_program(), dataset=dataset) """ if dataset == None: raise RuntimeError("dataset is need and should be initialized") if not isinstance(self.place, core.CPUPlace): raise RuntimeError("train_from_dataset is verified on CPUPlace" "We will open CUDAPlace in the future") scope, trainer = self._prepare_trainer( program=program, dataset=dataset, scope=scope, thread=thread, debug=debug, fetch_list=fetch_list, fetch_info=fetch_info, print_period=print_period) trainer._gen_trainer_desc() dataset._prepare_to_run() if debug: self._dump_debug_info(program=program, trainer=trainer) self._default_executor.run_from_dataset(program.desc, scope, dataset.dataset, trainer._desc()) return None