diff --git a/python/tvm/ansor/__init__.py b/python/tvm/ansor/__init__.py
index 977e100e63c6..90a11820d159 100644
--- a/python/tvm/ansor/__init__.py
+++ b/python/tvm/ansor/__init__.py
@@ -40,7 +40,7 @@
     workload_key_to_dag, make_workload_key_func
 from .task_scheduler import TaskScheduler, SimpleTaskScheduler
 from .dispatcher import DispatchContext, ApplyConfig, ApplyHistoryBest as apply_history_best, \
-    FallbackContext, clear_fallback_cache, ApplyGraphBest
+    FallbackContext
 from .relay_integration import extract_from_program, extract_from_multiple_program, \
     finish_layout_rewrite, prepare_layout_rewrite, auto_schedule_topi
 from .env import GLOBAL_SCOPE
diff --git a/python/tvm/ansor/auto_schedule.py b/python/tvm/ansor/auto_schedule.py
index acf8982d6e89..e8108a067b2e 100644
--- a/python/tvm/ansor/auto_schedule.py
+++ b/python/tvm/ansor/auto_schedule.py
@@ -97,7 +97,6 @@ class MetaTileRewritePolicy(SearchPolicy):
     seed: int
         Random seed
     """
-
     def __init__(self,
                  program_cost_model,
                  params=None,
diff --git a/python/tvm/ansor/compute_dag.py b/python/tvm/ansor/compute_dag.py
index f35c9d8221f3..6304c7bb0e0a 100644
--- a/python/tvm/ansor/compute_dag.py
+++ b/python/tvm/ansor/compute_dag.py
@@ -53,6 +53,8 @@ def get_init_state(self):
 
     def apply_steps_from_state(self, state, layout_rewrite_level=LayoutRewriteLevel.NO_REWRITE):
         """
+        Apply transform steps according to the history of a state
+
         Parameters
         ----------
         state : StateObject
@@ -68,6 +70,8 @@ def apply_steps_from_state(self, state, layout_rewrite_level=LayoutRewriteLevel.
 
     def print_python_code_from_state(self, state):
         """
+        Print transform steps in the history of a state as TVM's python schedule primitive
+
         Parameters
         ----------
         state : StateObject
@@ -81,16 +85,29 @@ def print_python_code_from_state(self, state):
 
     def infer_bound_from_state(self, state):
         """
+        Infer bound for a state
+
         Parameters
         ----------
         state : StateObject
 
         Returns
         -------
-        state : StateObject
+        state : State
         """
         state_obj = state if isinstance(state, StateObject) else state.state_object
         return State(_ffi_api.ComputeDAGInferBoundFromState(self, state_obj), self)
 
     def rewrite_layout_from_state(self, state: State):
+        """
+        Rewrite the layout according to the transform steps in the history of a state
+
+        Parameters
+        ----------
+        state : StateObject
+
+        Returns
+        -------
+        state : StateObject
+        """
         return _ffi_api.ComputeDAGRewriteLayoutFromState(self, state)
diff --git a/python/tvm/ansor/cost_model/cost_model.py b/python/tvm/ansor/cost_model/cost_model.py
index 47ea5092b302..57cc53853b2e 100644
--- a/python/tvm/ansor/cost_model/cost_model.py
+++ b/python/tvm/ansor/cost_model/cost_model.py
@@ -26,18 +26,20 @@
 
 @tvm._ffi.register_object("ansor.CostModel")
 class CostModel(Object):
+    """The base class for cost model"""
     pass
 
 
 @tvm._ffi.register_object("ansor.RandomModel")
 class RandomModel(Object):
+    """A model returns random estimation for all inputs"""
     def __init__(self):
         self.__init_handle_by_constructor__(_ffi_api.RandomModel)
 
 
-# A random number generator func for c++'s RandomModel
 @tvm._ffi.register_func("ansor.cost_model.random_number")
 def random_number(n, return_ptr):
+    """ A random number generator func for c++'s RandomModel """
     if n == 0:
         return
     return_ptr = ctypes.cast(return_ptr, ctypes.POINTER(ctypes.c_float))
@@ -47,6 +49,7 @@ def random_number(n, return_ptr):
 
 @tvm._ffi.register_object("ansor.PythonBasedModel")
 class PythonBasedModel(CostModel):
+    """Base class for cost models implemented in python"""
     def __init__(self):
         def update_func(inputs, results):
             self.update(inputs, results)
diff --git a/python/tvm/ansor/cost_model/xgb_model.py b/python/tvm/ansor/cost_model/xgb_model.py
index fce3f16d18ba..42af17daae2c 100644
--- a/python/tvm/ansor/cost_model/xgb_model.py
+++ b/python/tvm/ansor/cost_model/xgb_model.py
@@ -16,16 +16,14 @@
 # under the License.
 
 """Cost model based on xgboost"""
-from typing import List
 import multiprocessing
 import logging
-import time
 from collections import defaultdict
 
 import numpy as np
 import xgboost as xgb
 
-from ...autotvm.tuner.xgboost_cost_model import get_rank, recall_curve, max_curve
+from tvm.autotvm.tuner.xgboost_cost_model import get_rank, recall_curve, max_curve
 from .cost_model import PythonBasedModel
 from ..feature import get_per_stmt_features_from_measure_pairs, get_per_stmt_features_from_states
 from ..serialization import LogReader
@@ -65,8 +63,8 @@ def __init__(self, verbose_eval=25, num_warmup_sample=100, seed=None):
             # todo(lmzheng): automatically decrease learning rate when the loss is too large
 
             'n_gpus': 0,
-            'n_threads': multiprocessing.cpu_count() / 2,
-            'silent': 0,
+            'nthread': multiprocessing.cpu_count() // 2,
+            'verbosity': 0,
             'seed': seed or 43,
             'disable_default_eval_metric': 1
         }
@@ -180,7 +178,7 @@ def pack_sum_xgbmatrix_for_prediction(xs):
             x_flatten.append(row)
             pack_ids.append(ct)
 
-    return xgb.DMatrix(x_flatten), pack_ids
+    return xgb.DMatrix(np.array(x_flatten)), pack_ids
 
 
 def pack_sum_xgbmatrix(xs, ys, gids=None, weights=None):
@@ -214,7 +212,7 @@ def pack_sum_xgbmatrix(xs, ys, gids=None, weights=None):
                 y_flatten.append(y)
                 pack_ids.append(ct)
 
-    ret = xgb.DMatrix(x_flatten, y_flatten)
+    ret = xgb.DMatrix(np.array(x_flatten), y_flatten)
     if weights is not None:
         ret.set_weight(weights_flatten)
     dmatrix_context.put('pack_ids', ret, np.array(pack_ids))
diff --git a/python/tvm/ansor/dispatcher.py b/python/tvm/ansor/dispatcher.py
index 0ef07197ea92..0c07fd141bd2 100644
--- a/python/tvm/ansor/dispatcher.py
+++ b/python/tvm/ansor/dispatcher.py
@@ -15,16 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-Template dispatcher module.
-
-A dispatcher is a function that can contains multiple behaviors.
-Its specific behavior is can be controlled by DispatchContext.
-
-DispatchContext is used in two ways, usually via different implementation
-of the DispatchContext base class.
-
-- During search, we can use it to pass the current proposal from tuner.
-- During evaluation, we can use it to set pick the best policy.
+The global context that dispatches best configurations to workloads
 """
 # pylint: disable=invalid-name
 
@@ -33,9 +24,7 @@
 import logging
 
 import numpy as np
-from decorator import decorate
 
-from tvm import target as _target
 from tvm.tir.expr import FloatImm
 
 logger = logging.getLogger('auto_scheduler')
@@ -44,9 +33,6 @@
 class DispatchContext(object):
     """
     Base class of dispatch context.
-
-    DispatchContext enables the target and workload
-    specific dispatch mechanism for templates.
     """
     current = None 
 
@@ -55,7 +41,7 @@ def __init__(self):
 
     def query(self, target, workload):
         """
-        Query the context to get the specific config for a template.
+        Query the context to get the specific config for a workload.
         If cannot find the result inside this context, this function will query it
         from the upper contexts.
 
@@ -63,22 +49,20 @@ def query(self, target, workload):
         ----------
         target: Target
             The current target
-        workload : Workload
-            The current workload.
+        workload : str
+            The current workload
 
         Returns
         -------
-        cfg : State or str
-            The specific state for auto scheduler.
+        cfg : State
+            The schedule configuration for the workload
         """
         ret = self._query_inside(target, workload)
-        #if ret is None:
-        #    ret = self._old_ctx.query(target, workload)
         return ret
 
     def update(self, target, workload, cfg):
         """
-        Update context with a specific config.
+        Update the config for a workload
 
         Parameters
         ----------
@@ -86,46 +70,14 @@ def update(self, target, workload, cfg):
             The current target
         workload : Workload
             The current workload.
-        cfg : State or str
-            The specific state for auto scheduler.
-
-        Note
-        ----
-        This interface is for cases when TVM decides to replace an operator in the graph.
-        For example, `AlterOpLayout` pass (enables when `opt_level = 3`) replaces `NCHW`
-        convolution with `NCHW[x]c` implementation on x86 CPUs.
-        Thus in TOPI, we first query schedule using original `NCHW` workload,
-        then update the dispatcher with the new `NCHW[x]c` workload.
-        So that later on, `NCHW[x]c` convolution can get schedule from the dispatcher using
-        its own workload directly.
-
-        .. code-block:: python
-
-            @conv2d_alter_layout.register("cpu")
-            def _alter_conv2d_layout(attrs, inputs, tinfo):
-                workload = get_conv2d_workload(...)
-                dispatch_ctx = auto_scheduler.DispatchContext.current
-                target = tvm.target.current_target()
-                config = dispatch_ctx.query(target, workload)
-
-                # Get conv2d_NCHWc workload from config
-                # new_workload = ...
-                # new_inputs = ...
-                # new_attrs = ...
-
-                # Store altered operator's config
-                dispatch_ctx.update(target, new_workload, config)
-                return sym.contrib.conv2d_NCHWc(*new_inputs, **new_attrs)
-
-        We directly store `config` back because `conv2d_NCHW` and `conv2d_NCHWc`
-        share the same schedule parameters.
-        One can construct a new `State` if this is not the case.
+        cfg : State
+            The schedule configuration for the workload
         """
         raise NotImplementedError()
 
     def _query_inside(self, target, workload):
         """
-        Query the context to get the specific config for a template.
+        Query the context to get the specific config for a workload.
         This function only query config inside this context.
 
         Parameters
@@ -138,7 +90,7 @@ def _query_inside(self, target, workload):
         Returns
         -------
         cfg : State or str
-            The specific state for auto scheduler.
+            The schedule configuration for the workload
         """
         raise NotImplementedError()
 
@@ -151,78 +103,13 @@ def __exit__(self, ptype, value, trace):
         DispatchContext.current = self._old_ctx
 
 
-def dispatcher(fworkload):
-    """Wrap a workload dispatcher function.
-
-    Parameters
-    ----------
-    fworkload : function
-        The workload extraction function from arguments.
-
-    Returns
-    -------
-    fdispatcher : function
-        A wrapped dispatcher function, which will
-        dispatch based on DispatchContext and
-        the current workload.
-    """
-    dispatch_dict = {}
-    func_name = fworkload.__name__
-
-    def register(key, func=None, override=False):
-        """Register template function.
-
-        Parameters
-        ----------
-        key : str or List of str
-            The template key to identify the template
-            under this dispatcher.
-        func : function
-            The function to be registered.
-            The first argument of the function is always
-            cfg returned by DispatchContext,
-            the rest arguments are the same as the fworkload.
-        override : bool
-            Whether override existing registration.
-
-        Returns
-        -------
-        The register function if necessary.
-        """
-        if isinstance(key, str):
-            key = [key]
-
-        def _do_reg(myf):
-            for x in key:
-                if x in dispatch_dict and not override:
-                    raise ValueError(
-                        "Key %s is already registered for %s" % (x, func_name))
-                dispatch_dict[x] = myf
-            return myf
-
-        if func:
-            return _do_reg(func)
-        return _do_reg
-
-    def dispatch_func(func, *args, **kwargs):
-        """The wrapped dispatch function"""
-        tgt = _target.current_target()
-        workload = func(*args, **kwargs)
-        cfg = DispatchContext.current.query(tgt, workload)
-        return dispatch_dict['direct'](cfg, *args, **kwargs)
-
-    fdecorate = decorate(fworkload, dispatch_func)
-    fdecorate.register = register
-    return fdecorate
-
-
 class ApplyConfig(DispatchContext):
-    """Apply a deterministic config entity for all queries.
+    """Apply a deterministic config for all queries.
 
     Parameters
     ----------
     config : State
-        The specific state for auto scheduler.
+        The schedule configuration
     """
     def __init__(self, config):
         super(ApplyConfig, self).__init__()
@@ -361,9 +248,7 @@ def update(self, target, workload, state):
 class FallbackContext(DispatchContext):
     """
     A fallback dispatch context.
-
-    Any tunable template can be called under this context.
-    This is the root context.
+    This is used as the root context.
     """
 
     def __init__(self):
@@ -387,7 +272,7 @@ def _query_inside(self, target, workload):
                 logger.warning(msg)
         cfg = None
 
-        # cache this config
+        # cache this config to avoid duplicated warning message
         self.memory[key] = cfg
         return cfg
 
@@ -412,91 +297,3 @@ def update(self, target, workload, cfg):
 
 
 DispatchContext.current = FallbackContext()
-
-
-def clear_fallback_cache(target, workload):
-    """Clear fallback cache. Pass the same argument as _query_inside to this function
-    to clean the cache.
-
-    Parameters
-    ----------
-    target: Target
-        The current target
-    workload : Workload
-        The current workload.
-
-    Note
-    ----
-    This is used in alter_op_layout to clear the bad cache created before call topi compute function
-    """
-    context = DispatchContext.current
-    while not isinstance(context, FallbackContext):
-        context = context._old_ctx
-    context.clear_cache(target, workload)
-
-
-class ApplyGraphBest(DispatchContext):
-    """Load the graph level tuning optimal schedules.
-
-    The input records should be in the ascending order of
-    node index for target operator. Usually this can be obtained
-    with graph tuner.
-
-    This context maintains an internal counter to indicate the current
-    node index.
-    """
-    def __init__(self, records):
-        """
-        Parameters
-        ----------
-        records : str or iterator of (MeasureInput, MeasureResult)
-            Collection of tuning records.
-            If is str, then it should be the filename of a records log file.
-                   Each row of this file is an encoded record pair.
-            Otherwise, it is an iterator.
-        """
-        from . import load_from_file
-
-        super(ApplyGraphBest, self).__init__()
-        if isinstance(records, str):
-            records = load_from_file(records)
-        self._records = list(records)
-        self._counter = 0
-        self._global_cfg_dict = {}
-
-    def _query_inside(self, target, workload):
-        """
-        Query the context to get config from records.
-
-        Parameters
-        ----------
-        target : Target
-            The current target
-        workload : Workload
-            The current workload.
-
-        Returns
-        -------
-        cfg : State or str
-            The specific state for auto scheduler.
-        """
-        if self._counter < len(self._records):
-            cfg = self._records[self._counter][0].config
-            self._counter += 1
-            self.update(target, workload, cfg)
-            return cfg
-        key = (str(target), workload)
-        if key not in self._global_cfg_dict:
-            msg = "Config for target=%s, workload=%s is missing in ApplyGraphBest context. " \
-                  "A fallback configuration is used, which may bring great performance " \
-                  "regression." % (target, workload)
-            logger.warning(msg)
-            cfg = None
-            self._global_cfg_dict[key] = cfg
-        else:
-            cfg = self._global_cfg_dict[key]
-        return cfg
-
-    def update(self, target, workload, cfg):
-        key = (str(target), workload)
-        self._global_cfg_dict[key] = cfg
diff --git a/python/tvm/ansor/env.py b/python/tvm/ansor/env.py
index 9e44ad66048b..0f35f92acbbc 100644
--- a/python/tvm/ansor/env.py
+++ b/python/tvm/ansor/env.py
@@ -1,5 +1,23 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
 """ The scope to store global variables in ansor """
 
+
 class AutoschedulerGlobalScope(object):
     def __init__(self):
         self.topi_in_compute_rewrite_mode = False
diff --git a/python/tvm/ansor/feature.py b/python/tvm/ansor/feature.py
index 9496533da6cc..d9f6d297f1af 100644
--- a/python/tvm/ansor/feature.py
+++ b/python/tvm/ansor/feature.py
@@ -17,7 +17,6 @@
 
 """"
 Python API for Feature extraction.
-The specification of features can be found in `autoscheduler_doc/per_stage_feature.md`
 """
 
 from typing import List, Tuple
diff --git a/python/tvm/ansor/measure.py b/python/tvm/ansor/measure.py
index 3d9c33860cae..f00fe672505d 100644
--- a/python/tvm/ansor/measure.py
+++ b/python/tvm/ansor/measure.py
@@ -230,7 +230,8 @@ def __init__(self, key, host, port, priority=1,
 
 
 class LocalRPCMeasureContext:
-    """ A context wrapper for RPCRunner.
+    """ A context wrapper for running RPCRunner locally.
+    This will launch a local RPC Tracker and local RPC Server.
 
     Parameters
     ----------
@@ -276,10 +277,10 @@ class MeasureErrorNo(object):
     """Error type for MeasureResult"""
     NO_ERROR = 0              # No error
     INSTANTIATION_ERROR = 1   # Errors happen when apply transform steps from init state
-    # Errors happen when compiling code on host (e.g. tvm.build)
+                              # Errors happen when compiling code on host (e.g. tvm.build)
     COMPILE_HOST = 2
     COMPILE_DEVICE = 3        # Errors happen when compiling code on device
-    # (e.g. OpenCL JIT on the device)
+                              # (e.g. OpenCL JIT on the device)
     RUNTIME_DEVICE = 4        # Errors happen when run program on device
     WRONG_ANSWER = 5          # Answer is wrong when compared to a reference output
     BUILD_TIMEOUT = 6         # Timeout during compilation
@@ -288,6 +289,7 @@ class MeasureErrorNo(object):
 
 
 def make_error_msg():
+    """Get the error message from traceback"""
     error_msg = str(traceback.format_exc())
     if len(error_msg) > MAX_ERROR_MSG_LEN:
         error_msg = error_msg[:MAX_ERROR_MSG_LEN//2] + \
diff --git a/python/tvm/ansor/serialization.py b/python/tvm/ansor/serialization.py
index 97903b38bb0b..1bd9d8cf64e6 100644
--- a/python/tvm/ansor/serialization.py
+++ b/python/tvm/ansor/serialization.py
@@ -64,6 +64,7 @@ def __iter__(self):
                 break
             yield ret[0], ret[1]  # (input, result)
 
+
 def load_from_file(filename: str):
     """Load measurement records from a file"""
     return zip(*LogReader(filename).read_lines())
diff --git a/python/tvm/ansor/task_scheduler.py b/python/tvm/ansor/task_scheduler.py
index 89b4afd84e86..3d4d9624d7c2 100644
--- a/python/tvm/ansor/task_scheduler.py
+++ b/python/tvm/ansor/task_scheduler.py
@@ -147,13 +147,12 @@ def __init__(self,
     def tune(self, tune_option: TuneOption, search_policy: Union[str, List[SearchPolicy]] = 'default'):
         """ Tune tasks.
 
-            Notice: This method does not have return value, make sure to set `LogToFile`
-            measure callback in `tune_option`.
+        Notice: This method does not have return value, make sure to set `LogToFile`
+        measure callback in `tune_option`.
 
         Parameters
         ----------
         tune_option: TuneOption
-
         search_policy: Str or List[SearchPolicy]
         """
         # init members
diff --git a/python/tvm/ansor/workload_registry.py b/python/tvm/ansor/workload_registry.py
index fccdcf8864be..bcf8269b9490 100644
--- a/python/tvm/ansor/workload_registry.py
+++ b/python/tvm/ansor/workload_registry.py
@@ -15,7 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 
-
 """
 Workload registration and serialization.
 
diff --git a/src/ansor/serialization.cc b/src/ansor/serialization.cc
index 454305c04ef5..2d8379f56a5f 100644
--- a/src/ansor/serialization.cc
+++ b/src/ansor/serialization.cc
@@ -55,7 +55,6 @@ template <>
 struct Handler<std::vector<::tvm::ansor::Stage> > {
   inline static void Write(dmlc::JSONWriter* writer,
                            const std::vector<::tvm::ansor::Stage> & data) {
-    // todo(lmzheng): support serialization of Stage
     writer->BeginArray(false);
     writer->EndArray();
   }
@@ -456,7 +455,7 @@ namespace ansor {
 TVM_REGISTER_OBJECT_TYPE(LogToFileNode);
 TVM_REGISTER_OBJECT_TYPE(LogReaderNode);
 
-const std::string ANSOR_LOG_VERSION = "v0.1";    // NOLINT(*)
+const std::string ANSOR_LOG_VERSION = "v0.2";    // NOLINT(*)
 
 MeasureCallback LogToFileNode::make(std::string filename) {
   auto node = make_object<LogToFileNode>();
diff --git a/tests/python/unittest/test_ansor_feature.py b/tests/python/unittest/test_ansor_feature.py
index bb19b84a970d..bcc7683b3f4a 100644
--- a/tests/python/unittest/test_ansor_feature.py
+++ b/tests/python/unittest/test_ansor_feature.py
@@ -148,4 +148,3 @@ def test_gpu_feature():
     test_cpu_matmul()
     test_cpu_fusion()
     test_gpu_feature()
-