diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py index 6febedc8e1811..d8f01f41fa118 100644 --- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py +++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py @@ -123,7 +123,7 @@ def add_sparse_table(self, table_id, strategy): support_accessor_class = [ 'DownpourFeatureValueAccessor', 'DownpourCtrAccessor', 'DownpourSparseValueAccessor', 'DownpourCtrDoubleAccessor', - 'DownpourUnitAccessor' + 'DownpourUnitAccessor', 'DownpourDoubleUnitAccessor' ] if strategy.get('sparse_accessor_class') is not None: accessor_class = strategy.get('sparse_accessor_class') @@ -254,7 +254,7 @@ def add_sparse_table(self, table_id, strategy): table2.param = 2 table2.converter = converter table2.deconverter = deconverter - elif accessor_class == 'DownpourUnitAccessor': + elif accessor_class == 'DownpourUnitAccessor' or accessor_class == 'DownpourDoubleUnitAccessor': self.add_sparse_table_common_config(table, strategy) self.add_sparse_optimizer(table.accessor.embed_sgd_param, strategy, "embed_") @@ -380,7 +380,7 @@ def add_data_norm_table(self, table_id, learning_rate, param_var, grad_var, table.accessor.fea_dim = fea_dim def add_sparse_optimizer(self, sgd, strategy, prefix): - optimizer_name = strategy.get(prefix + "sparse_optimizer", "adam") + optimizer_name = strategy.get(prefix + "sparse_optimizer", "adagrad") sgd.name = optimizer_name if optimizer_name == "naive": sgd.naive.learning_rate = \ @@ -394,6 +394,19 @@ def add_sparse_optimizer(self, sgd, strategy, prefix): strategy.get(prefix + 'sparse_learning_rate', 0.05) sgd.adagrad.initial_range = \ strategy.get(prefix + 'sparse_initial_range', 1e-4) + if prefix == "embed_": + sgd.adagrad.initial_range = 0 + sgd.adagrad.initial_g2sum = strategy.get( + prefix + 'sparse_initial_g2sum', 3) + bounds = strategy.get(prefix + 'sparse_weight_bounds', [-10, 10]) + sgd.adagrad.weight_bounds.extend(bounds) + elif optimizer_name == "std_adagrad": + sgd.adagrad.learning_rate = \ + strategy.get(prefix + 'sparse_learning_rate', 0.05) + sgd.adagrad.initial_range = \ + strategy.get(prefix + 'sparse_initial_range', 1e-4) + if prefix == "embed_": + sgd.adagrad.initial_range = 0 sgd.adagrad.initial_g2sum = strategy.get( prefix + 'sparse_initial_g2sum', 3) bounds = strategy.get(prefix + 'sparse_weight_bounds', [-10, 10]) diff --git a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py index 8447d0f96089d..5035d406d5b09 100644 --- a/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py +++ b/python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py @@ -319,6 +319,7 @@ def _minimize(self, # user do not have to set it in config_fleet if accessor == "DownpourFeatureValueAccessor" \ or accessor == "DownpourCtrAccessor" \ + or accessor == "DownpourDoubleUnitAccessor" \ or accessor == "DownpourUnitAccessor": if st.get("sparse_embedx_dim") is not None \ and st["sparse_embedx_dim"] != emb_to_size[key] - 3: @@ -534,7 +535,7 @@ def _minimize(self, if server._server.downpour_server_param.downpour_table_param[ 0].accessor.accessor_class in [ "DownpourCtrAccessor", "DownpourCtrDoubleAccessor", - "DownpourUnitAccessor" + "DownpourUnitAccessor", "DownpourDoubleUnitAccessor" ]: opt_info["dump_slot"] = True elif server._server.downpour_server_param.downpour_table_param[