# 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 logging import os import multiprocessing import sys import warnings import numpy as np from .wrapped_decorator import signature_safe_contextmanager import six from .data_feeder import convert_dtype from .framework import Program, default_main_program, Variable, convert_np_dtype_to_dtype_ from . import core from . import compiler from .. import compat as cpt from .trainer_factory import TrainerFactory from .trainer_factory import FetchHandlerMonitor __all__ = ['Executor', 'global_scope', 'scope_guard'] g_scope = core.Scope() InferNativeConfig = core.NativeConfig InferAnalysisConfig = core.AnalysisConfig 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. Examples: .. code-block:: python import paddle.fluid as fluid import numpy fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid.CPUPlace()) numpy.array(fluid.global_scope().find_var("data").get_tensor()) """ return g_scope def _switch_scope(scope): global g_scope ex = g_scope g_scope = scope return ex @signature_safe_contextmanager def scope_guard(scope): """ This function switches scope through python `with` statement. Scope records the mapping between variable names and variables ( :ref:`api_guide_Variable` ), similar to brackets in programming languages. If this function is not invoked, all variables and variable names are recorded in the default global scope. When users need to create variables with the same name, they need to switch scopes through this function if they do not want the mapping of variables with the same name to be overwritten. After switching through the `with` statement, all variables created in the `with` block will be assigned to a new scope. Parameters: scope: The new scope. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy new_scope = fluid.Scope() with fluid.scope_guard(new_scope): fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid.CPUPlace()) numpy.array(new_scope.find_var("data").get_tensor()) """ 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: .. code-block:: python import paddle.fluid as fluid import numpy new_scope = fluid.Scope() with fluid.scope_guard(new_scope): fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid.CPUPlace()) tensor = new_scope.find_var("data").get_tensor() fluid.executor.as_numpy(tensor) # or numpy.array(new_scope.find_var("data").get_tensor()) 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.") if tensor._is_initialized(): return np.array(tensor) else: return None def dtype_is_compatible_with(first, second): """ Returns True if the first dtype can be compatible the second one. Currently, we require the two dtype's have to be same. Args: dtype (np.dtype|VarType|str): The type of data: float32, int64, etc. Returns: True if the two types are same. """ if not isinstance(first, core.VarDesc.VarType): first = convert_np_dtype_to_dtype_(first) if not isinstance(second, core.VarDesc.VarType): second = convert_np_dtype_to_dtype_(second) return first == second def dimension_is_compatible_with(first, second): """ Returns True if the two dimensions are compatible. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimentions are same. 3. For negative number or 'None' in a dimention, it means unknown so it is compatible with any number. Args: first (list/tuple): integers representing shape. "None" or negative number means unknown. second (list/tuple): integers representing shape. "None" or negative number means unknown. Returns: True if the two dimensions are compatible. """ dim_len = len(first) if dim_len != len(second): return False for i in range(dim_len): if first[i] is None or first[i] < 0: continue if second[i] is None or second[i] < 0: continue if first[i] != second[i]: return False return True def check_feed_shape_type(var, feed): """ Returns True if the variable doesn't require feed check or it is compatible with the shape and have same dtype as the feeded value. A dimension is compatible with the other if: 1. The length of the dimensions are same. 2. Each non-negative number of the two dimentions are same. 3. For negative number or 'None' in a dimention, it means unknown so it is compatible with any number. Args: var (Variable): the Variable object feed (LoDTensor): the feeded value, which must be a LoDTensor Returns: True if the shape and dtype of variable is compatible with the feed value Raises: ValueError: if the shape or dtype of the variable is not compatible with the feed value """ if var.desc.need_check_feed(): if not dimension_is_compatible_with(feed.shape(), var.shape): raise ValueError( 'The feeded Variable %r should have dimensions = %d, shape = ' '%r, but received feeded shape %r' % (var.name, len(var.shape), var.shape, feed.shape())) if not dtype_is_compatible_with(feed._dtype(), var.dtype): var_dtype_format = convert_dtype(var.dtype) if isinstance( var.dtype, core.VarDesc.VarType) else var.dtype feed_dtype_format = convert_dtype(feed._dtype()) if isinstance( feed._dtype(), core.VarDesc.VarType) else feed._dtype() raise ValueError( 'The data type of feeded Variable %r must be %r, but received %r' % (var.name, var_dtype_format, feed_dtype_format)) return True 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. 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. 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_strong_program_cache_key(program, feed, fetch_list): return str(id(program)) + _get_program_cache_key(feed, fetch_list) 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 class FetchHandler(object): def __init__(self, fetch_target_names, period_secs=60, return_np=True): self.fetch_target_names = fetch_target_names self.period_secs = period_secs self.return_np = return_np def handler(self, fetch_target_vars): return @staticmethod def help(): print(""" class FetchHandlerExamlpe(FetchHandler): def handler(self, fetch_target_vars): b_auc = fetch_target_vars[0] g_auc = fetch_target_vars[1] print("b_auc: {}, g_auc: {} at time: {}".format(b_auc, g_auc, time.ctime())) """) class Executor(object): """ An Executor in Python, supports single/multiple-GPU running, and single/multiple-CPU running. When construction the Executor, the device is required. Args: place(fluid.CPUPlace()|fluid.CUDAPlace(n)): This parameter represents the executor run on which device. Returns: Executor Examples: .. 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() exe = fluid.Executor(place) train_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(train_program, startup_program): data = fluid.data(name='X', shape=[None, 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) # Run the startup program once and only once. # Not need to optimize/compile the startup program. startup_program.random_seed=1 exe.run(startup_program) # Run the main program directly without compile. x = numpy.random.random(size=(10, 1)).astype('float32') loss_data, = exe.run(train_program, feed={"X": x}, fetch_list=[loss.name]) # Or, compiled the program and run. See `CompiledProgram` # for more detail. # 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) compiled_prog = compiler.CompiledProgram( train_program).with_data_parallel( loss_name=loss.name) loss_data, = exe.run(compiled_prog, feed={"X": x}, fetch_list=[loss.name]) """ def __init__(self, place): self.place = place self.program_caches = dict() self.ctx_caches = dict() self.scope_caches = dict() self.var_caches = dict() p = core.Place() p.set_place(self.place) self._default_executor = core.Executor(p) self._closed = False def _get_var_cache(self, program_cache_key): return self.var_caches.get(program_cache_key, None) def _get_scope_cache(self, program_cache_key): return self.scope_caches.get(program_cache_key, None) def _get_ctx_cache(self, program_cache_key): return self.ctx_caches.get(program_cache_key, None) 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_ctx_cache(self, ctx_cache_key, ctx): self.ctx_caches[ctx_cache_key] = ctx def _add_scope_cache(self, scope_cache_key, scope): self.scope_caches[scope_cache_key] = scope def _add_var_cache(self, var_cache_key, var): self.var_caches[var_cache_key] = var 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 global_block = program.global_block() for op in 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) var = global_block.var(feed_target_name) check_feed_shape_type(var, cur_feed) 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? ''' def close(self): """ Close the executor. This interface is used for distributed training (PServers mode). This executor can not be used after calling the interface, because this interface releases resources associated with the current Trainer. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid cpu = fluid.CPUPlace() exe = fluid.Executor(cpu) # execute training or testing 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 # TODO(zhenghuihuang): quantization uses Graph in CompiledProgram # instead of program. We will add support for checking Vars in Graph need_check_feed = program._program is not None if need_check_feed: global_block = program._program.global_block() 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 split # it is fast in CPU assert isinstance( feed[feed_name], np.ndarray ), \ "The input({}) should be numpy.array, but not {}.".format( feed_name, type(feed[feed_name])) feed_tensor.set(feed[feed_name], core.CPUPlace()) if need_check_feed: var = global_block.var(feed_name) check_feed_shape_type(var, feed_tensor) 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() assert isinstance(each[feed_name], np.ndarray), \ "The input({}) should be numpy.array, but not {}.".format( feed_name, type(each[feed_name])) tmp.set(tensor, program._places[i]) tensor = tmp if need_check_feed: var = global_block.var(feed_name) check_feed_shape_type(var, tensor) 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)) tensors = exe.run(fetch_var_names)._move_to_list() return as_numpy(tensors) if return_numpy else tensors 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 the specified :code:`Program` or :code:`CompiledProgram`. It should be noted that the executor will execute all the operators in :code:`Program` or :code:`CompiledProgram` without pruning some operators of the :code:`Program` or :code:`CompiledProgram` according to fetch_list. And you could specify the scope to store the :code:`Variables` during the executor running if the scope is not set, the executor will use the global scope, i.e. :code:`fluid.global_scope()`. Args: program(Program|CompiledProgram): This parameter represents the :code:`Program` or :code:`CompiledProgram` to be executed. If this parameter is not provided, that parameter is None, the program will be set to :code:`fluid.default_main_program()`. The default is None. feed(list|dict): This parameter represents the input variables of the model. If it is single card training, the feed is dict type, and if it is multi-card training, the parameter feed can be dict or list type variable. If the parameter type is dict, the data in the feed will be split and sent to multiple devices (CPU/GPU), that is to say, the input data will be evenly sent to different devices, so you should make sure the number of samples of the current mini-batch must be greater than the number of places; if the parameter type is list, those data are copied directly to each device, so the length of this list should be equal to the number of places. The default is None. fetch_list(list): This parameter represents the variables that need to be returned after the model runs. The default is None. feed_var_name(str): This parameter represents the name of the input variable of the feed operator. The default is "feed". fetch_var_name(str): This parameter represents the name of the output variable of the fetch operator. The default is "fetch". scope(Scope): the scope used to run this program, you can switch it to different scope. default is :code:`fluid.global_scope()` return_numpy(bool): This parameter indicates whether convert the fetched variables (the variable specified in the fetch list) to numpy.ndarray. if it is False, the type of the return value is a list of :code:`LoDTensor`. The default is True. use_program_cache(bool): This parameter indicates whether the input :code:`Program` is cached. If the parameter is True, the model may run faster in the following cases: the input program is :code:`fluid.Program`, and the parameters(program, feed variable name and fetch_list variable) of this interface remains unchanged during running. The default is False. Returns: List: The fetched result list. NOTES: 1. If it is multi-card running and the feed parameter is dict type, the input data will be evenly sent to different cards. For example, using two GPUs to run the model, the input sample number is 3, that is, [0, 1, 2], the sample number on GPU0 is 1, that is, [0], and the sample number on GPU1 is 2, that is, [1, 2]. If the number of samples is less than the number of devices, the program will throw an exception, so when running the model, you should make sure that the number of samples of the last batch of the data set should be greater than the number of CPU cores or GPU cards, if it is less than, it is recommended that the batch be discarded. 2. If the number of CPU cores or GPU cards available is greater than 1, the fetch results are spliced together in dimension 0 for the same variable values (variables in fetch_list) on different devices. Examples: .. code-block:: python import paddle.fluid as fluid import numpy # First create the Executor. place = fluid.CPUPlace() # fluid.CUDAPlace(0) exe = fluid.Executor(place) data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) adam = fluid.optimizer.Adam() adam.minimize(loss) # Run the startup program once and only once. 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]) """ try: return self._run_impl( program=program, 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) except Exception as e: if not isinstance(e, core.EOFException): warnings.warn( "The following exception is not an EOF exception.") six.reraise(*sys.exc_info()) def _run_impl(self, program, feed, fetch_list, feed_var_name, fetch_var_name, scope, return_numpy, use_program_cache): if self._closed: raise RuntimeError("Attempted to use a closed Executor") use_default_main_program = program is None if program is None: program = default_main_program() if isinstance(program, Program) and \ len(program.global_block().ops) == 0: error_info = "The current program is empty." if use_default_main_program: error_info += " Maybe you should pass the Program or the CompiledProgram manually." warnings.warn(error_info) if scope is None: scope = global_scope() if fetch_list is not None: if isinstance(fetch_list, Variable) or isinstance(fetch_list, str): fetch_list = [fetch_list] assert isinstance(fetch_list, tuple) or isinstance(fetch_list, list), \ "Currently , The fetch_list type only should be list or tuple, \n"\ "but the input type is {}. For more information please refer to \n"\ "the executor.run(...).".format(type(fetch_list)) else: fetch_list = [] compiled = isinstance(program, compiler.CompiledProgram) # For backward compatibility, run directly. if not compiled: return self._run_program( program, 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_inference: return self._run_inference(program._executor, feed) else: return self._run_parallel( program, scope=scope, feed=feed, fetch_list=fetch_list, fetch_var_name=fetch_var_name, return_numpy=return_numpy) def _run_program(self, program, 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))) assert program is not None, "The program should not be Empty" if not isinstance(program, Program): raise TypeError( "Executor requires Program as its Parameter. But you passed in %s" % (type(program))) if use_program_cache: cache_key = _get_strong_program_cache_key(program, feed, fetch_list) cached_program = self._get_program_cache(cache_key) cached_ctx = self._get_ctx_cache(cache_key) cached_scope = self._get_scope_cache(cache_key) cached_var = self._get_var_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) fetch_list_str = list(map(_to_name_str, fetch_list)) cached_ctx = self._default_executor.prepare_ctx_cache( cached_program.desc, 0, fetch_list_str, False) cached_var = self._default_executor.create_variables( cached_program.desc, scope, 0) # currently, we cache program, vars, sub_scope here # we suppose that in a life cycle of training, a user # will not create many programs. So, here the basic # rule of caching is to cache all unseen (program, var, scope) # when a user use use_program_cache. cached_scope = scope.new_scope() self._add_ctx_cache(cache_key, cached_ctx) self._add_var_cache(cache_key, cached_var) self._add_scope_cache(cache_key, cached_scope) program = cached_program ctx = cached_ctx scope = cached_scope var = cached_var else: 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) if not use_program_cache: self._default_executor.run(program.desc, scope, 0, True, True, fetch_var_name) else: self._default_executor.run_cached_prepared_ctx(ctx, scope, False, False, False) arr = scope.find_var(fetch_var_name).get_lod_tensor_array() tensors = arr._move_to_list() if return_numpy: return as_numpy(tensors) else: return tensors 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(str(trainer)) if program._fleet_opt: with open("fleet_desc.prototxt", "w") as fout: fout.write(str(program._fleet_opt["fleet_desc"])) def _adjust_pipeline_resource(self, pipeline_opt, dataset, pipeline_num): filelist_length = len(dataset.dataset.get_filelist()) if filelist_length < pipeline_num: pipeline_num = filelist_length print( "Pipeline training: setting the pipeline num to %d is enough because there are only %d files" % (filelist_length, filelist_length)) if filelist_length < pipeline_num * pipeline_opt["concurrency_list"][0]: print( "Pipeline training: setting the 1st element in concurrency_list to %d is enough because there are only %d files" % (filelist_length // pipeline_num, filelist_length)) pipeline_opt["concurrency_list"][ 0] = filelist_length // pipeline_num dataset.set_thread(pipeline_opt["concurrency_list"][0] * pipeline_num) return pipeline_num 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: # TODO: Need a better way to distinguish and specify different execution mode if program._pipeline_opt: trainer = TrainerFactory()._create_trainer( program._pipeline_opt) else: trainer = TrainerFactory()._create_trainer(program._fleet_opt) trainer._set_program(program) else: if program._pipeline_opt: trainer = TrainerFactory()._create_trainer( program.program._pipeline_opt) 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 def _run_from_dataset(self, program=None, dataset=None, scope=None, thread=0, is_infer=False, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): if dataset is None: raise RuntimeError("dataset is need and should be initialized") if program._pipeline_opt: thread = self._adjust_pipeline_resource(program._pipeline_opt, dataset, thread) dataset._prepare_to_run() if fetch_handler is not None: fetch_instance = fetch_handler elif fetch_handler is None and fetch_list is not None: class FH(FetchHandler): def handler(self, fetch_target_vars): for i in range(len(fetch_target_vars)): print("{}: \n {}\n".format(fetch_info[i], fetch_target_vars[i])) fetch_target_names = [var.name for var in fetch_list] fetch_instance = FH(fetch_target_names, period_secs=print_period, return_np=False) else: fetch_instance = FetchHandler([]) scope, trainer = self._prepare_trainer( program=program, dataset=dataset, scope=scope, thread=thread, debug=debug) trainer._set_infer(is_infer) trainer._gen_trainer_desc() self._dump_debug_info(program=program, trainer=trainer) trainer_instance = self._default_executor.init_for_dataset( program.desc, trainer._desc(), scope, dataset.dataset) scope0 = trainer_instance.get_worker_scope(0) fetch_monitor = FetchHandlerMonitor(scope0, fetch_instance) fetch_monitor.start() self._default_executor.run_from_dataset(trainer_instance) fetch_monitor.stop() dataset._finish_to_run() return None def infer_from_dataset(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): """ 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 fetch_handler(FetchHandler): a user define class for fetch output. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CPUPlace() # you can set place = fluid.CUDAPlace(0) to use gpu exe = fluid.Executor(place) x = fluid.layers.data(name="x", shape=[10, 10], dtype="int64") y = fluid.layers.data(name="y", shape=[1], dtype="int64", lod_level=1) dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var([x, y]) dataset.set_thread(1) filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) exe.infer_from_dataset(program=fluid.default_main_program(), dataset=dataset) """ return self._run_from_dataset(program, dataset, scope, thread, True, debug, fetch_list, fetch_info, print_period, fetch_handler) def train_from_dataset(self, program=None, dataset=None, scope=None, thread=0, debug=False, fetch_list=None, fetch_info=None, print_period=100, fetch_handler=None): """ 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 fetch_handler(FetchHandler): a user define class for fetch output. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid place = fluid.CPUPlace() # you can set place = fluid.CUDAPlace(0) to use gpu exe = fluid.Executor(place) x = fluid.layers.data(name="x", shape=[10, 10], dtype="int64") y = fluid.layers.data(name="y", shape=[1], dtype="int64", lod_level=1) dataset = fluid.DatasetFactory().create_dataset() dataset.set_use_var([x, y]) dataset.set_thread(1) filelist = [] # you should set your own filelist, e.g. filelist = ["dataA.txt"] dataset.set_filelist(filelist) exe.run(fluid.default_startup_program()) exe.train_from_dataset(program=fluid.default_main_program(), dataset=dataset) """ return self._run_from_dataset(program, dataset, scope, thread, False, debug, fetch_list, fetch_info, print_period, fetch_handler)