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98 lines
4.4 KiB
98 lines
4.4 KiB
import ps_pb2 as pslib
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class Server(object):
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def __init__(self):
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pass
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class Worker(object):
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def __init__(self):
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pass
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class DownpourServer(Server):
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def __init__(self):
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self.server_ = pslib.ServerParameter()
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self.server_.downpour_server_param.service_param.start_server_port = 0
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self.server_.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
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self.server_.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
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self.server_.downpour_server_param.service_param.service_class = "DownpourPsService"
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self.server_.downpour_server_param.service_param.start_server_port = 0
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self.server_.downpour_server_param.service_param.server_thread_num = 12
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def add_sparse_table(self, table_id, learning_rate,
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slot_key_vars, slot_value_var):
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table = self.server_.downpour_server_param.downpour_table_param.add()
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table.table_id = table_id
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table.table_class = "DownpourSparseTable"
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table.type = pslib.PS_SPARSE_TABLE
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table.accessor.accessor_class = "DownpourFeatureValueAccessor"
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table.accessor.sparse_sgd_param.learning_rate = learning_rate
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table.accessor.sparse_sgd_param.initial_g2sum = 3
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table.accessor.sparse_sgd_param.initial_range = 1e-4
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table.accessor.sparse_sgd_param.weight_bounds.extend([-10, 10])
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table.accessor.embedx_dim = 8
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table.accessor.embedx_threshold = 5
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table.accessor.fea_dim = 11
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#table.accessor.fea_dim = abs(reduce(lambda x, y: x * y,
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# slot_value_var[0].shape, 1))
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table.accessor.downpour_accessor_param.nonclk_coeff = 0.1
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table.accessor.downpour_accessor_param.click_coeff = 2
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table.accessor.downpour_accessor_param.base_threshold = 0.2
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table.accessor.downpour_accessor_param.delta_threshold = 0.15
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table.accessor.downpour_accessor_param.delta_keep_days = 31
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table.accessor.downpour_accessor_param.show_click_decay_rate = 0.999
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table.accessor.downpour_accessor_param.delete_threshold = 0.8
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def add_dense_table(self, table_id, learning_rate,
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param_var, grad_var):
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table = self.server_.downpour_server_param.downpour_table_param.add()
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table.table_id = table_id
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table.table_class = "DownpourDenseTable"
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table.type = pslib.PS_DENSE_TABLE
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table.accessor.accessor_class = "DownpourDenseValueAccessor"
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table.accessor.dense_sgd_param.name = "adam"
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table.accessor.dense_sgd_param.adam.learning_rate = learning_rate
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table.accessor.dense_sgd_param.adam.avg_decay_rate = 0.999993
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table.accessor.dense_sgd_param.adam.ada_decay_rate = 0.9999
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table.accessor.dense_sgd_param.adam.ada_epsilon = 1e-8
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table.accessor.dense_sgd_param.adam.mom_decay_rate = 0.99
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table.accessor.dense_sgd_param.naive.learning_rate = 0.0002
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fea_dim = 0
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for param in filter(lambda x: x.name.find("embedding") == -1, param_var):
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fea_dim += reduce(lambda x, y: x * y, param.shape, 1)
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table.accessor.fea_dim = fea_dim
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def get_desc(self):
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return self.server_
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class DownpourWorker(Worker):
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def __init__(self, window):
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self.window = window
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self.worker_ = pslib.DownpourTrainerParameter()
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#self.worker_.pull_dense_per_batch = window
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#self.worker_.push_dense_per_batch = window
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def add_sparse_table(self, table_id, learning_rate,
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slot_key_vars, slot_value_vars):
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table = self.worker_.sparse_table.add()
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table.table_id = table_id
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table.slot_key.extend(
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[var.name for var in slot_key_vars])
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table.slot_value.extend(
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[var.name for var in slot_value_vars])
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table.slot_gradient.extend(
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[var.name + "@GRAD" for var in slot_value_vars])
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def add_dense_table(self, table_id, learning_rate,
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param_vars, grad_vars):
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table = self.worker_.dense_table.add()
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table.table_id = table_id
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table.dense_variable_name.extend(filter(lambda x: x.find("embedding") == -1, [p.name for p in param_vars]))
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table.dense_gradient_variable_name.extend(filter(lambda x: x.find("embedding") == -1, [g.name for g in grad_vars]))
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def get_desc(self):
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return self.worker_
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