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169 lines
7.6 KiB
169 lines
7.6 KiB
# 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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from .node import DownpourServer
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from .node import DownpourWorker
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from ..backward import append_backward
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import ps_pb2 as pslib
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from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
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from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_inputs
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from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_outputs
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from google.protobuf import text_format
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class DownpourSGD(object):
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"""
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Distributed optimizer of downpour stochastic gradient descent
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Standard implementation of Google's Downpour SGD
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in Large Scale Distributed Deep Networks
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Args:
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learning_rate (float): the learning rate used to update parameters. \
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Can be a float value
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Examples:
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.. code-block:: python
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opt = fluid.DistributedOptimizer(sgd_opt)
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opt.minimize()
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downpour_sgd = fluid.distributed.DownpourSGD(learning_rate=0.2)
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downpour_sgd.minimize(cost)
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"""
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def __init__(self, learning_rate=0.001, window=1):
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# todo(guru4elephant): add more optimizers here as argument
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# todo(guru4elephant): make learning_rate as a variable
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self.learning_rate_ = learning_rate
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self.window_ = window
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self.type = "downpour"
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self.data_norm_name = [
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".batch_size", ".batch_square_sum", ".batch_sum",
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".batch_size@GRAD", ".batch_square_sum@GRAD", ".batch_sum@GRAD"
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]
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def minimize(self,
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losses,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None):
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"""
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DownpounSGD is a distributed optimizer so
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that user can call minimize to generate backward
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operators and optimization operators within minimize function
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Args:
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loss(Variable): loss variable defined by user
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startup_program(Program): startup program that defined by user
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parameter_list(str list): parameter names defined by users
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no_grad_set(set): a set of variables that is defined by users
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so that these variables do not need gradient computation
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Returns:
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[ps_param, worker_skipped_ops]
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ps_param: parameter server protobuf desc
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worker_skipped_ops: operator names that need
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to be skipped during execution
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"""
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if not isinstance(losses, list):
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raise ValueError('losses is a list, just lick [model.cost]')
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table_name = find_distributed_lookup_table(losses[0].block.program)
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prefetch_slots = find_distributed_lookup_table_inputs(
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losses[0].block.program, table_name)
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prefetch_slots_emb = find_distributed_lookup_table_outputs(
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losses[0].block.program, table_name)
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ps_param = pslib.PSParameter()
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server = DownpourServer()
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worker = DownpourWorker(self.window_)
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sparse_table_index = 0
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server.add_sparse_table(sparse_table_index, self.learning_rate_,
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prefetch_slots, prefetch_slots_emb)
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worker.add_sparse_table(sparse_table_index, self.learning_rate_,
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prefetch_slots, prefetch_slots_emb)
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dense_table_index = 1
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program_configs = []
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param_grads_list = []
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for loss_index in range(len(losses)):
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program_config = ps_param.trainer_param.program_config.add()
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program_config.program_id = str(
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id(losses[loss_index].block.program))
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program_config.pull_sparse_table_id.extend([sparse_table_index])
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program_config.push_sparse_table_id.extend([sparse_table_index])
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params_grads = sorted(
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append_backward(losses[loss_index], parameter_list,
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no_grad_set),
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key=lambda x: x[0].name)
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param_grads_list.append(params_grads)
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params = []
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grads = []
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data_norm_params = []
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data_norm_grads = []
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for i in params_grads:
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is_data_norm_data = False
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for data_norm_name in self.data_norm_name:
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if i[0].name.endswith(data_norm_name):
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is_data_norm_data = True
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data_norm_params.append(i[0])
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if not is_data_norm_data:
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params.append(i[0])
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for i in params_grads:
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is_data_norm_data = False
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for data_norm_grad in self.data_norm_name:
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if i[0].name.endswith(data_norm_grad):
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is_data_norm_data = True
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data_norm_grads.append(i[1])
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if not is_data_norm_data:
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grads.append(i[1])
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server.add_dense_table(dense_table_index, self.learning_rate_,
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params, grads)
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worker.add_dense_table(dense_table_index, self.learning_rate_,
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params, grads)
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program_config.pull_dense_table_id.extend([dense_table_index])
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program_config.push_dense_table_id.extend([dense_table_index])
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if len(data_norm_params) != 0 and len(data_norm_grads) != 0:
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dense_table_index += 1
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server.add_data_norm_table(dense_table_index,
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self.learning_rate_,
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data_norm_params, data_norm_grads)
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worker.add_dense_table(dense_table_index, self.learning_rate_,
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data_norm_params, data_norm_grads)
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program_config.pull_dense_table_id.extend([dense_table_index])
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program_config.push_dense_table_id.extend([dense_table_index])
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dense_table_index += 1
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program_configs.append(program_config)
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ps_param.server_param.CopyFrom(server.get_desc())
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ps_param.trainer_param.CopyFrom(worker.get_desc())
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for program_config in program_configs:
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ps_param.trainer_param.program_config.extend([program_config])
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# Todo(guru4elephant): figure out how to support more sparse parameters
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# currently only support lookup_table
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worker_skipped_ops = ["lookup_table", "lookup_table_grad"]
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ps_param.trainer_param.skip_op.extend(worker_skipped_ops)
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# all fleet operations should be defined in operators in the future
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# we want to return an object here containing:
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# 1) worker execution strategy
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# 2) pserver execution strategy
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# 3) fleet configurations
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# 4) skipped operators in runtime
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# 5) distributed optimization
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opt_info = {}
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opt_info["trainer"] = "DistMultiTrainer"
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opt_info["device_worker"] = "DownpourSGD"
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opt_info["optimizer"] = "DownpourSGD"
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opt_info["fleet_desc"] = ps_param
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opt_info["worker_skipped_ops"] = worker_skipped_ops
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for loss in losses:
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loss.block.program._fleet_opt = opt_info
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return None, param_grads_list
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