Fix xgb error & Simplify dispatcher (apache#35)

Author: merrymercy

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

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,

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)

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)

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))