[Feature] Add hyperband and randomsearch from Hyperbandster

federatedscope/autotune/algos.py

@@ -70,9 +70,9 @@ def get_scheduler(init_cfg):
         init_cfg (federatedscope.core.configs.config.CN): configuration.
     """
 
-    if init_cfg.hpo.scheduler == 'rs':
-        scheduler = ModelFreeBase(init_cfg)
-    elif init_cfg.hpo.scheduler in ['sha', 'bo_kde', 'bohb', 'bo_gp', 'bo_rf']:
+    if init_cfg.hpo.scheduler in [
+            'sha', 'rs', 'bo_kde', 'bohb', 'hb', 'bo_gp', 'bo_rf'
+    ]:
         scheduler = SuccessiveHalvingAlgo(init_cfg)
     # elif init_cfg.hpo.scheduler == 'pbt':
     #     scheduler = PBT(init_cfg)

federatedscope/autotune/hpbandster.py

@@ -7,7 +7,7 @@
 import ConfigSpace as CS
 import hpbandster.core.nameserver as hpns
 from hpbandster.core.worker import Worker
-from hpbandster.optimizers import BOHB
+from hpbandster.optimizers import BOHB, HyperBand, RandomSearch
 from hpbandster.optimizers.iterations import SuccessiveHalving
 
 from federatedscope.autotune.utils import eval_in_fs
@@ -16,31 +16,41 @@
 logger = logging.getLogger(__name__)
 
 
+def clear_cache(working_folder):
+    # Clear cached ckpt
+    for name in os.listdir(working_folder):
+        if name.endswith('.pth'):
+            os.remove(osp(working_folder, name))
+
+
+class MyRandomSearch(RandomSearch):
+    def __init__(self, working_folder, **kwargs):
+        self.working_folder = working_folder
+        super(MyRandomSearch, self).__init__(**kwargs)
+
+
 class MyBOHB(BOHB):
     def __init__(self, working_folder, **kwargs):
         self.working_folder = working_folder
         super(MyBOHB, self).__init__(**kwargs)
 
     def get_next_iteration(self, iteration, iteration_kwargs={}):
-        # number of 'SH rungs'
-        s = self.max_SH_iter - 1 - (iteration % self.max_SH_iter)
-        # number of configurations in that bracket
-        n0 = int(np.floor((self.max_SH_iter) / (s + 1)) * self.eta**s)
-        ns = [max(int(n0 * (self.eta**(-i))), 1) for i in range(s + 1)]
         if os.path.exists(self.working_folder):
-            self.clear_cache()
-        return (SuccessiveHalving(
-            HPB_iter=iteration,
-            num_configs=ns,
-            budgets=self.budgets[(-s - 1):],
-            config_sampler=self.config_generator.get_config,
-            **iteration_kwargs))
+            clear_cache(self.working_folder)
+        return super(MyBOHB, self).get_next_iteration(iteration,
+                                                      iteration_kwargs)
+
 
-    def clear_cache(self):
-        # Clear cached ckpt
-        for name in os.listdir(self.working_folder):
-            if name.endswith('.pth'):
-                os.remove(osp(self.working_folder, name))
+class MyHyperBand(HyperBand):
+    def __init__(self, working_folder, **kwargs):
+        self.working_folder = working_folder
+        super(MyHyperBand, self).__init__(**kwargs)
+
+    def get_next_iteration(self, iteration, iteration_kwargs={}):
+        if os.path.exists(self.working_folder):
+            clear_cache(self.working_folder)
+        return super(MyHyperBand,
+                     self).get_next_iteration(iteration, iteration_kwargs)
 
 
 class MyWorker(Worker):
@@ -102,7 +112,15 @@ def run_hpbandster(cfg, scheduler):
         'max_budget': cfg.hpo.sha.budgets[-1],
         'working_folder': cfg.hpo.working_folder
     }
-    optimizer = MyBOHB(**opt_kwargs)
+    if cfg.hpo.scheduler in ['rs', 'wrap_rs']:
+        optimizer = MyRandomSearch(**opt_kwargs)
+    elif cfg.hpo.scheduler in ['hb', 'wrap_hb']:
+        optimizer = MyHyperBand(**opt_kwargs)
+    elif cfg.hpo.scheduler in ['bo_kde', 'bohb', 'wrap_bo_kde', 'wrap_bohb']:
+        optimizer = MyBOHB(**opt_kwargs)
+    else:
+        raise ValueError
+
     if cfg.hpo.sha.iter != 0:
         n_iterations = cfg.hpo.sha.iter
     else:

federatedscope/hpo.py

@@ -38,10 +38,11 @@
     # global_cfg.merge_from_list(args.opts)
 
     scheduler = get_scheduler(init_cfg)
-    if init_cfg.hpo.scheduler in ['rs', 'sha', 'wrap_sha']:
+    if init_cfg.hpo.scheduler in ['sha', 'wrap_sha']:
         _ = scheduler.optimize()
     elif init_cfg.hpo.scheduler in [
-            'bo_kde', 'bohb', 'wrap_bo_kde', 'wrap_bohb'
+            'rs', 'bo_kde', 'hb', 'bohb', 'wrap_rs', 'wrap_bo_kde', 'wrap_hb',
+            'wrap_bohb'
     ]:
         from federatedscope.autotune.hpbandster import run_hpbandster
         run_hpbandster(init_cfg, scheduler)

scripts/example_configs/femnist/avg/hb.yaml

@@ -0,0 +1,51 @@
+use_gpu: True
+device: 3
+outdir: hb_femnist_avg
+early_stop:
+  patience: 100
+seed: 12345
+federate:
+  mode: standalone
+  total_round_num: 500
+  sample_client_rate: 1.0
+  share_local_model: True
+  online_aggr: True
+data:
+  root: data/
+  type: femnist
+  splits: [0.6,0.2,0.2]
+  batch_size: 16
+  subsample: 0.05
+  transform: [['ToTensor'], ['Normalize', {'mean': [0.1307], 'std': [0.3081]}]]
+  num_workers: 0
+model:
+  type: convnet2
+  hidden: 2048
+  out_channels: 62
+  dropout: 0.5
+train:
+  batch_or_epoch: epoch
+  local_update_steps: 1
+  optimizer:
+    lr: 0.01
+    weight_decay: 0.0
+grad:
+  grad_clip: 5.0
+criterion:
+  type: CrossEntropyLoss
+trainer:
+  type: cvtrainer
+eval:
+  freq: 1
+  metrics: ['acc', 'correct', 'f1']
+  split: ['test', 'val', 'train']
+hpo:
+  scheduler: hb
+  num_workers: 0
+  ss: 'scripts/example_configs/femnist/avg/ss.yaml'
+  sha:
+    budgets: [ 9, 81 ]
+    elim_rate: 3
+    iter: 12
+  metric: 'client_summarized_weighted_avg.val_avg_loss'
+  working_folder: hb_femnist_avg

scripts/example_configs/femnist/avg/hb_wrap.yaml

@@ -0,0 +1,61 @@
+use_gpu: True
+device: 3
+outdir: hb_wrap_femnist_avg
+early_stop:
+  patience: 100
+seed: 12345
+federate:
+  mode: standalone
+  total_round_num: 500
+  sample_client_rate: 1.0
+  share_local_model: True
+  online_aggr: True
+  use_diff: True
+data:
+  root: data/
+  type: femnist
+  splits: [0.6,0.2,0.2]
+  batch_size: 16
+  subsample: 0.05
+  transform: [['ToTensor'], ['Normalize', {'mean': [0.1307], 'std': [0.3081]}]]
+  num_workers: 0
+model:
+  type: convnet2
+  hidden: 2048
+  out_channels: 62
+  dropout: 0.5
+train:
+  batch_or_epoch: epoch
+  local_update_steps: 1
+  optimizer:
+    lr: 0.01
+    weight_decay: 0.0
+grad:
+  grad_clip: 5.0
+criterion:
+  type: CrossEntropyLoss
+trainer:
+  type: cvtrainer
+eval:
+  freq: 1
+  metrics: ['acc', 'correct', 'f1']
+  split: ['test', 'val', 'train']
+hpo:
+  scheduler: wrap_hb
+  num_workers: 0
+  init_cand_num: 100
+  ss: 'scripts/example_configs/femnist/avg/ss.yaml'
+  sha:
+    budgets: [ 9, 81 ]
+    elim_rate: 3
+    iter: 12
+  table:
+    num: 27
+  fedex:
+    sched: 'aggressive'
+    use: True
+    diff: False
+    eta0: -1.0
+    gamma: 0.0
+  metric: 'client_summarized_weighted_avg.val_avg_loss'
+  working_folder: hb_wrap_femnist_avg

scripts/example_configs/femnist/avg/rs.yaml

@@ -40,12 +40,13 @@ eval:
   metrics: ['acc', 'correct', 'f1']
   split: ['test', 'val', 'train']
 hpo:
-  scheduler: sha
+  scheduler: rs
   num_workers: 0
   init_cand_num: 10
   ss: 'scripts/example_configs/femnist/avg/ss.yaml'
   sha:
-    budgets: [50]
-    elim_rate: 10
+    budgets: [ 50, 50 ]
+    elim_rate: 3
+    iter: 50
   metric: 'client_summarized_weighted_avg.val_avg_loss'
   working_folder: rs_femnist_avg

scripts/example_configs/femnist/avg/rs_wrap.yaml

@@ -41,13 +41,14 @@ eval:
   metrics: ['acc', 'correct', 'f1']
   split: ['test', 'val', 'train']
 hpo:
-  scheduler: wrap_sha
+  scheduler: wrap_rs
   num_workers: 0
-  init_cand_num: 10
+  init_cand_num: 100
   ss: 'scripts/example_configs/femnist/avg/ss.yaml'
   sha:
-    budgets: [50]
-    elim_rate: 10
+    budgets: [ 50, 50 ]
+    elim_rate: 3
+    iter: 50
   table:
     num: 27
   fedex: