update prompt-tuning codes for nlp tasks

Author: cheneydon

.github/workflows/test_prompt.yml renamed to .github/workflows/test_fedsp.yml

@@ -1,4 +1,4 @@
-name: UnitTests for Prompt Tuning
+name: UnitTests for FedSP
 
 on:
   schedule:
@@ -44,7 +44,7 @@ jobs:
     - name: Test Prompt Tuning
       run: |
         python ../../main.py \
-          --cfg federatedscope/nlp/prompt_tuning/baseline/config_alter_train.yaml \
+          --cfg federatedscope/nlp/fedsp/baseline/config_alter_train.yaml \
           data.dataset_name arc_challenge \
           data.batch_size 1 \
           data.max_seq_len 32 \
@@ -53,8 +53,8 @@ jobs:
           federate.total_round_num 2 \
           federate.make_global_train True \
           federate.pl_init_kd True \
-          federate.pl_kd_cfg_file federatedscope/nlp/prompt_tuning/baseline/config_init_kd_test.yaml \
-          federate.pl_global_cfg_file federatedscope/nlp/prompt_tuning/baseline/config_global.2.yaml \
+          federate.pl_kd_cfg_file federatedscope/nlp/fedsp/baseline/config_init_kd_test.yaml \
+          federate.pl_global_cfg_file federatedscope/nlp/fedsp/baseline/config_global.2.yaml \
           model.use_fp16 True \
           model.model_type facebook/opt-1.3b \
           model.use_prefix_prj False \

federatedscope/core/auxiliaries/data_builder.py

@@ -31,7 +31,7 @@
         'dblp_org', 'csbm.*?', 'fb15k-237', 'wn18', 'adult', 'abalone',
         'credit', 'blog'
     ],  # Dummy for FL dataset
-    'RawDataTranslator': ['hetero_nlp_tasks', 'pl_data'],
+    'RawDataTranslator': ['hetero_nlp_tasks', 'fedsp_data'],
 }
 DATA_TRANS_MAP = RegexInverseMap(TRANS_DATA_MAP, None)
 

federatedscope/core/auxiliaries/model_builder.py

@@ -197,9 +197,9 @@ def get_model(model_config, local_data=None, backend='torch', role='client'):
     elif model_config.type.lower() in ['atc_model']:
         from federatedscope.nlp.hetero_tasks.model import ATCModel
         model = ATCModel(model_config)
-    elif model_config.type.lower() in ['pl_model']:
-        from federatedscope.nlp.prompt_tuning.model import PLModel
-        model = PLModel(model_config, role=role)
+    elif model_config.type.lower() in ['fedsp_model']:
+        from federatedscope.nlp.fedsp.model import FedSPModel
+        model = FedSPModel(model_config, role=role)
     else:
         raise ValueError('Model {} is not provided'.format(model_config.type))
 

federatedscope/core/auxiliaries/trainer_builder.py

@@ -29,7 +29,7 @@
     "cltrainer": "CLTrainer",
     "lptrainer": "LPTrainer",
     "atc_trainer": "ATCTrainer",
-    "pl_trainer": "PLTrainer",
+    "fedsp_trainer": "FedSPTrainer",
 }
 
 
@@ -158,7 +158,7 @@ def get_trainer(model=None,
             dict_path = "federatedscope.mf.trainer.trainer"
         elif config.trainer.type.lower() in ['atc_trainer']:
             dict_path = "federatedscope.nlp.hetero_tasks.trainer"
-        elif config.trainer.type.lower() in ["pl_trainer"]:
+        elif config.trainer.type.lower() in ["fedsp_trainer"]:
             dict_path = "federatedscope.nlp.prompt_tuning.trainer"
         else:
             raise ValueError

federatedscope/core/auxiliaries/worker_builder.py

@@ -76,9 +76,9 @@ def get_client_cls(cfg):
         from federatedscope.nlp.hetero_tasks.worker import ATCClient
         return ATCClient
 
-    if cfg.trainer.type.lower() == 'pl_trainer':
-        from federatedscope.nlp.prompt_tuning.worker import PLClient
-        return PLClient
+    if cfg.trainer.type.lower() == 'fedsp_trainer':
+        from federatedscope.nlp.fedsp.worker import FedSPClient
+        return FedSPClient
 
     if cfg.federate.method.lower() in constants.CLIENTS_TYPE:
         client_type = constants.CLIENTS_TYPE[cfg.federate.method.lower()]
@@ -199,9 +199,9 @@ def get_server_cls(cfg):
         from federatedscope.nlp.hetero_tasks.worker import ATCServer
         return ATCServer
 
-    if cfg.trainer.type.lower() == 'pl_trainer':
-        from federatedscope.nlp.prompt_tuning.worker import PLServer
-        return PLServer
+    if cfg.trainer.type.lower() == 'fedsp_trainer':
+        from federatedscope.nlp.fedsp.worker import FedSPServer
+        return FedSPServer
 
     if cfg.federate.method.lower() in constants.SERVER_TYPE:
         server_type = constants.SERVER_TYPE[cfg.federate.method.lower()]

federatedscope/core/data/utils.py

@@ -94,9 +94,9 @@ def load_dataset(config, client_cfgs=None):
         from federatedscope.nlp.hetero_tasks.dataloader import \
             load_heteroNLP_data
         dataset, modified_config = load_heteroNLP_data(config, client_cfgs)
-    elif 'pl_data' in config.data.type.lower():
-        from federatedscope.nlp.prompt_tuning.dataloader import load_pl_data
-        dataset, modified_config = load_pl_data(config)
+    elif 'fedsp_data' in config.data.type.lower():
+        from federatedscope.nlp.fedsp.dataloader import load_fedsp_data
+        dataset, modified_config = load_fedsp_data(config)
     elif '@' in config.data.type.lower():
         from federatedscope.core.data.utils import load_external_data
         dataset, modified_config = load_external_data(config)

federatedscope/nlp/prompt_tuning/README.md renamed to federatedscope/nlp/fedsp/README.md

@@ -11,20 +11,20 @@ federate:
   pl_alter_train: True
   pl_save_to: ckpt
 data:
-  type: pl_data
+  type: fedsp_data
   batch_size: 16
   max_seq_len: 1024
   num_workers: 0
 model:
-  type: pl_model
+  type: fedsp_model
   model_type: facebook/opt-1.3b
   num_server_layers: 24
   prefix_hidden_size: 512
   server_freeze_param: ['model']
   alter_model_param: ['model']
   alter_prompt_param: ['prefix_encoder']
 trainer:
-  type: pl_trainer
+  type: fedsp_trainer
 train:
   batch_or_epoch: batch
   local_update_steps: 10

federatedscope/nlp/prompt_tuning/__init__.py renamed to federatedscope/nlp/fedsp/__init__.py

@@ -10,18 +10,18 @@ federate:
   make_global_eval: True
   pl_save_to: ckpt
 data:
-  type: pl_data
+  type: fedsp_data
   batch_size: 16
   max_seq_len: 1024
   num_workers: 0
 model:
-  type: pl_model
+  type: fedsp_model
   model_type: facebook/opt-1.3b
   num_server_layers: 24
   server_freeze_param: ['model']
   client_freeze_param: ['model']
 trainer:
-  type: pl_trainer
+  type: fedsp_trainer
 train:
   batch_or_epoch: batch
   local_update_steps: 10