fixed the key comparison in the Bayesian strategy (#1484)

Author: yiliu30

neural_compressor/strategy/bayesian.py

@@ -23,6 +23,7 @@
 from sklearn.gaussian_process import GaussianProcessRegressor
 
 from collections import OrderedDict
+from copy import deepcopy
 
 from ..utils import logger
 from .strategy import strategy_registry, TuneStrategy
@@ -104,7 +105,10 @@ def params_to_tune_configs(self, params):
                 op_tuning_cfg[op_name_type] = configs[0]
             else:
                 op_tuning_cfg[op_name_type] = configs[min(len(configs) - 1, int(params[op_name_type[0]]))]
-        calib_sampling_size = calib_sampling_size_lst[min(len(configs) - 1, int(params['calib_sampling_size']))]
+        if len(calib_sampling_size_lst) > 1:
+            calib_sampling_size = calib_sampling_size_lst[min(len(configs) - 1, int(params['calib_sampling_size']))]
+        else:
+            calib_sampling_size = calib_sampling_size_lst[0]
         op_tuning_cfg['calib_sampling_size'] = calib_sampling_size
         return op_tuning_cfg
 
@@ -115,7 +119,6 @@ def next_tune_cfg(self):
         """
         params = None
         pbounds = {} 
-        from copy import deepcopy
         tuning_space = self.tuning_space
         calib_sampling_size_lst = tuning_space.root_item.get_option_by_name('calib_sampling_size').options
         op_item_dtype_dict, quant_mode_wise_items, initial_op_tuning_cfg = self.initial_tuning_cfg()
@@ -126,7 +129,8 @@ def next_tune_cfg(self):
         for op_name_type, configs in self.op_configs.items():
             if len(configs) > 1:
                 pbounds[op_name_type[0]] = (0, len(configs))
-        pbounds['calib_sampling_size'] = (0, len(calib_sampling_size_lst))
+        if len(calib_sampling_size_lst) > 1:
+            pbounds['calib_sampling_size'] = (0, len(calib_sampling_size_lst))
         if len(pbounds) == 0:
             yield self.params_to_tune_configs(params)
             return
@@ -225,10 +229,11 @@ def __init__(self, pbounds, random_seed=9527):
         """
         self.random_seed = random_seed
         # Get the name of the parameters
-        self._keys = sorted(pbounds)
+        names = list(pbounds.keys())
+        self._keys = deepcopy(names)
         # Create an array with parameters bounds
         self._bounds = np.array(
-            [item[1] for item in sorted(pbounds.items(), key=lambda x: x[0])],
+            [pbounds[name] for name in names],
             dtype=np.float
         )
 
@@ -275,7 +280,7 @@ def params_to_array(self, params):
             assert set(params) == set(self.keys)
         except AssertionError:
             raise ValueError(
-                "Parameters' keys ({}) do ".format(sorted(params)) +
+                "Parameters' keys ({}) do ".format(list(params.keys())) +
                 "not match the expected set of keys ({}).".format(self.keys)
             )
         return np.asarray([params[key] for key in self.keys])