[Ansor][AutoTVM v2.0] Phase 1: Rename namspace form auto_schedule to auto_scheduler

CMakeLists.txt

@@ -185,7 +185,7 @@ assign_source_group("Include" ${GROUP_INCLUDE})
 
 # Source file lists
 file(GLOB_RECURSE COMPILER_SRCS
-    src/auto_schedule/*.cc
+    src/auto_scheduler/*.cc
     src/node/*.cc
     src/ir/*.cc
     src/arith/*.cc

python/tvm/auto_schedule/_ffi_api.py → python/tvm/auto_scheduler/_ffi_api.py

@@ -15,8 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 
-""" Register FFI APIs from C++ for the namespace tvm.auto_schedule. """
+""" Register FFI APIs from C++ for the namespace tvm.auto_scheduler. """
 import tvm._ffi
 
 
-tvm._ffi._init_api("auto_schedule", __name__)
+tvm._ffi._init_api("auto_scheduler", __name__)

python/tvm/auto_schedule/auto_schedule.py → python/tvm/auto_scheduler/auto_schedule.py

@@ -34,7 +34,7 @@
 from . import _ffi_api
 
 
-@tvm._ffi.register_object("auto_schedule.HardwareParams")
+@tvm._ffi.register_object("auto_scheduler.HardwareParams")
 class HardwareParams(Object):
     """ The parameters of target hardware used to guide the search policy
 
@@ -55,7 +55,7 @@ def __init__(self, num_cores, vector_unit_bytes, cache_line_bytes):
                                             vector_unit_bytes, cache_line_bytes)
 
 
-@tvm._ffi.register_object("auto_schedule.SearchTask")
+@tvm._ffi.register_object("auto_scheduler.SearchTask")
 class SearchTask(Object):
     """ The computation information and hardware parameters for a specific schedule search task.
 
@@ -79,12 +79,12 @@ def __init__(self, dag, workload_key, target, target_host=None,
                                             hardware_params)
 
 
-@tvm._ffi.register_object("auto_schedule.SearchPolicy")
+@tvm._ffi.register_object("auto_scheduler.SearchPolicy")
 class SearchPolicy(Object):
     """ The base class of search policies. """
 
 
-@tvm._ffi.register_object("auto_schedule.EmptyPolicy")
+@tvm._ffi.register_object("auto_scheduler.EmptyPolicy")
 class EmptyPolicy(SearchPolicy):
     """ This is an example empty search policy which will always generate
     the init state of ComputeDAG.
@@ -93,7 +93,7 @@ def __init__(self):
         self.__init_handle_by_constructor__(_ffi_api.EmptyPolicy)
 
 
-@tvm._ffi.register_object("auto_schedule.TuningOptions")
+@tvm._ffi.register_object("auto_scheduler.TuningOptions")
 class TuningOptions(Object):
     """ This controls the options of performance tuning.
 
@@ -120,12 +120,12 @@ class TuningOptions(Object):
     measure_callbacks: Optional[List[MeasureCallback]]
       Callback functions called after each measurement.
       Candidates:
-        - auto_schedule.RecordToFile
+        - auto_scheduler.RecordToFile
     pre_search_callbacks: Optional[List[SearchCallback]]
       Callback functions called before the search process.
       Candidates:
-        - auto_schedule.PreloadMeasuredStates
-        - auto_schedule.PreloadCustomSketchRule
+        - auto_scheduler.PreloadMeasuredStates
+        - auto_scheduler.PreloadCustomSketchRule
         TODO(jcf94): Add these implementation in later PRs.
     """
     def __init__(self, num_measure_trials=0, early_stopping=None, num_measures_per_round=64,
@@ -136,7 +136,7 @@ def __init__(self, num_measure_trials=0, early_stopping=None, num_measures_per_r
                 builder = LocalBuilder()
             else:
                 raise ValueError("Invalid builder: " + builder)
-        elif not isinstance(builder, tvm.auto_schedule.measure.ProgramBuilder):
+        elif not isinstance(builder, tvm.auto_scheduler.measure.ProgramBuilder):
             raise ValueError("Invalid builder: " + builder +
                              " . TuningOptions expects a ProgramBuilder or string.")
 
@@ -145,7 +145,7 @@ def __init__(self, num_measure_trials=0, early_stopping=None, num_measures_per_r
                 runner = LocalRunner()
             else:
                 raise ValueError("Invalid runner: " + runner)
-        elif not isinstance(runner, tvm.auto_schedule.measure.ProgramRunner):
+        elif not isinstance(runner, tvm.auto_scheduler.measure.ProgramRunner):
             raise ValueError("Invalid runner: " + runner +
                              " . TuningOptions expects a ProgramRunner or string.")
 
@@ -176,7 +176,7 @@ def auto_schedule(task, search_policy='default', tuning_options=None):
     """
     if not isinstance(task, SearchTask):
         raise ValueError("Invalid task: " + task +
-                         " . `auto_schedule.auto_schedule` expects a SearchTask.")
+                         " . `auto_scheduler.auto_schedule` expects a SearchTask.")
 
     if isinstance(search_policy, str):
         if search_policy == 'default':
@@ -187,7 +187,7 @@ def auto_schedule(task, search_policy='default', tuning_options=None):
             raise ValueError("Invalid search policy: " + search_policy)
     elif not isinstance(search_policy, SearchPolicy):
         raise ValueError("Invalid search policy: " + search_policy +
-                         " . `auto_schedule.auto_schedule` expects a SearchPolicy or a string.")
+                         " . `auto_scheduler.auto_schedule` expects a SearchPolicy or a string.")
 
     sch, tensors = _ffi_api.AutoSchedule(task, search_policy,
                                          tuning_options if tuning_options else TuningOptions())

python/tvm/auto_schedule/compute_dag.py → python/tvm/auto_scheduler/compute_dag.py

@@ -30,7 +30,7 @@
 from . import _ffi_api
 
 
-@tvm._ffi.register_object("auto_schedule.ComputeDAG")
+@tvm._ffi.register_object("auto_scheduler.ComputeDAG")
 class ComputeDAG(Object):
     """
     The TVM Auto-scheduler computational graph and related program analyses.

python/tvm/auto_schedule/loop_state.py → python/tvm/auto_scheduler/loop_state.py

@@ -48,17 +48,17 @@
 from . import _ffi_api
 
 
-@tvm._ffi.register_object("auto_schedule.Iterator")
+@tvm._ffi.register_object("auto_scheduler.Iterator")
 class Iterator(Object):
     """ A loop iterator structure. """
 
 
-@tvm._ffi.register_object("auto_schedule.Stage")
+@tvm._ffi.register_object("auto_scheduler.Stage")
 class Stage(Object):
     """ A stage in the compute declaration. Similar to tvm.te.schedule.Stage. """
 
 
-@tvm._ffi.register_object("auto_schedule.State")
+@tvm._ffi.register_object("auto_scheduler.State")
 class StateObject(Object):
     """ The internal State object """
     def __eq__(self, other):

python/tvm/auto_schedule/measure.py → python/tvm/auto_scheduler/measure.py

@@ -54,12 +54,12 @@
 # This can avoid expensive serialization of TVM IR when using multiprocessing.Pool
 GLOBAL_BUILD_ARGUMENTS = None
 
-@tvm._ffi.register_object("auto_schedule.MeasureCallback")
+@tvm._ffi.register_object("auto_scheduler.MeasureCallback")
 class MeasureCallback(Object):
     """ The base class of measurement callback functions. """
 
 
-@tvm._ffi.register_object("auto_schedule.MeasureInput")
+@tvm._ffi.register_object("auto_scheduler.MeasureInput")
 class MeasureInput(Object):
     """ Store the input of a measurement.
 
@@ -74,7 +74,7 @@ def __init__(self, task, state):
         self.__init_handle_by_constructor__(_ffi_api.MeasureInput, task, state.state_object)
 
 
-@tvm._ffi.register_object("auto_schedule.BuildResult")
+@tvm._ffi.register_object("auto_scheduler.BuildResult")
 class BuildResult(Object):
     """ Store the result of a build.
 
@@ -99,7 +99,7 @@ def __init__(self, filename, args, error_no, error_msg, time_cost):
             _ffi_api.BuildResult, filename, args, error_no, error_msg, time_cost)
 
 
-@tvm._ffi.register_object("auto_schedule.MeasureResult")
+@tvm._ffi.register_object("auto_scheduler.MeasureResult")
 class MeasureResult(Object):
     """ Store the results of a measurement.
 
@@ -124,7 +124,7 @@ def __init__(self, costs, error_no, error_msg, all_cost, timestamp):
             error_msg, all_cost, timestamp)
 
 
-@tvm._ffi.register_object("auto_schedule.ProgramBuilder")
+@tvm._ffi.register_object("auto_scheduler.ProgramBuilder")
 class ProgramBuilder(Object):
     """ The base class of ProgramBuilders. """
 
@@ -145,7 +145,7 @@ def build(self, measure_inputs, verbose=1):
         return _ffi_api.ProgramBuilderBuild(self, measure_inputs, verbose)
 
 
-@tvm._ffi.register_object("auto_schedule.ProgramRunner")
+@tvm._ffi.register_object("auto_scheduler.ProgramRunner")
 class ProgramRunner(Object):
     """ The base class of ProgramRunners. """
 
@@ -168,7 +168,7 @@ def run(self, measure_inputs, build_results, verbose=1):
         return _ffi_api.ProgramRunnerRun(self, measure_inputs, build_results, verbose)
 
 
-@tvm._ffi.register_object("auto_schedule.LocalBuilder")
+@tvm._ffi.register_object("auto_scheduler.LocalBuilder")
 class LocalBuilder(ProgramBuilder):
     """ LocalBuilder use local CPU cores to build programs in parallel.
 
@@ -191,7 +191,7 @@ def __init__(self,
             _ffi_api.LocalBuilder, timeout, n_parallel, build_func)
 
 
-@tvm._ffi.register_object("auto_schedule.LocalRunner")
+@tvm._ffi.register_object("auto_scheduler.LocalRunner")
 class LocalRunner(ProgramRunner):
     """ LocalRunner that uses local CPU/GPU to measures the time cost of programs.
 
@@ -334,7 +334,7 @@ def timed_func():
     return res
 
 
-@tvm._ffi.register_func("auto_schedule.local_builder.build")
+@tvm._ffi.register_func("auto_scheduler.local_builder.build")
 def local_builder_build(inputs, timeout, n_parallel, build_func='default', verbose=1):
     """
     Build function of LocalBuilder to build the MeasureInputs to runnable modules.
@@ -376,7 +376,7 @@ def local_builder_build(inputs, timeout, n_parallel, build_func='default', verbo
 
     return results
 
-@tvm._ffi.register_func("auto_schedule.local_runner.run")
+@tvm._ffi.register_func("auto_scheduler.local_runner.run")
 def local_run(inputs, build_results, timeout, number, repeat, min_repeat_ms, cooldown_interval,
               verbose=1):
     """

python/tvm/auto_schedule/measure_record.py → python/tvm/auto_scheduler/measure_record.py

@@ -25,7 +25,7 @@
 from . import _ffi_api
 
 
-@tvm._ffi.register_object("auto_schedule.RecordToFile")
+@tvm._ffi.register_object("auto_scheduler.RecordToFile")
 class RecordToFile(MeasureCallback):
     """
     A measurement callback that writes measurement records into a file.
@@ -35,21 +35,21 @@ class RecordToFile(MeasureCallback):
     filename : str
         File name for this callback to write log to.
     """
-    def __init__(self, filename="auto_schedule_tuning.json"):
+    def __init__(self, filename="auto_scheduler_tuning.json"):
         self.__init_handle_by_constructor__(_ffi_api.RecordToFile, filename)
 
 
-@tvm._ffi.register_object("auto_schedule.RecordReader")
+@tvm._ffi.register_object("auto_scheduler.RecordReader")
 class RecordReader(Object):
     """
     Reader of the json log file.
 
     Parameters
     ----------
-    filename : str = "auto_schedule_tuning.json"
+    filename : str = "auto_scheduler_tuning.json"
         File name for this reader to load log from.
     """
-    def __init__(self, filename="auto_schedule_tuning.json"):
+    def __init__(self, filename="auto_scheduler_tuning.json"):
         self.__init_handle_by_constructor__(_ffi_api.RecordReader, filename)
 
     def read_lines(self, max_lines=None, skip_lines=0):

python/tvm/auto_schedule/utils.py → python/tvm/auto_scheduler/utils.py

@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-""" Common utilities for auto_schedule. """
+""" Common utilities for auto_scheduler. """
 
 from typing import Hashable
 import multiprocessing

python/tvm/auto_schedule/workload_registry.py → python/tvm/auto_scheduler/workload_registry.py

@@ -55,7 +55,7 @@ def register_workload(func_name, f=None, override=False):
 
     Examples
     --------
-    @auto_schedule.register_workload
+    @auto_scheduler.register_workload
     def matmul(N, M, K):
         A = te.placeholder((N, K), name='A')
         B = te.placeholder((K, M), name='B')
@@ -110,7 +110,7 @@ def make_workload_key(func, args):
 
     if not func_name in WORKLOAD_FUNC_REGISTRY:
         raise ValueError("%s is not registered. "  % func,
-                         "Please register it with @auto_schedule.register_workload")
+                         "Please register it with @auto_scheduler.register_workload")
 
     args = serialize_args(args)
 
@@ -137,11 +137,11 @@ def decode_workload_key_to_func_args(workload_key):
     workload = json.loads(workload_key)
     if not workload[0] in WORKLOAD_FUNC_REGISTRY:
         raise ValueError("%s is not registered. " % workload[0] +
-                         "Please register it with @auto_schedule.register_workload")
+                         "Please register it with @auto_scheduler.register_workload")
     return workload[0], deserialize_args(workload[1:])
 
 
-@tvm._ffi.register_func("auto_schedule.workload_key_to_tensors")
+@tvm._ffi.register_func("auto_scheduler.workload_key_to_tensors")
 def workload_key_to_tensors(workload_key):
     """ Get the input/output tensors from the workload key.
 

src/auto_schedule/auto_schedule.cc → src/auto_scheduler/auto_schedule.cc

@@ -18,16 +18,18 @@
  */
 
 /*!
- * \file auto_schedule/auto_schedule.cc
- * \brief The user interface of the TVM Auto-scheduler.
+ * \file auto_scheduler/auto_schedule.cc
+ * \brief The user interface of the TVM Auto-scheduler. This is the entry structure to get
+ * schedule search requirements from upper level (Python API), and returns a high performance
+ * schedule after search process.
  */
 
 #include "auto_schedule.h"
 
 #include <tvm/runtime/registry.h>
 
 namespace tvm {
-namespace auto_schedule {
+namespace auto_scheduler {
 
 TVM_REGISTER_NODE_TYPE(TuningOptionsNode);
 
@@ -61,7 +63,7 @@ std::pair<te::Schedule, Array<te::Tensor>> AutoSchedule(SearchTask task, SearchP
   return task->compute_dag.ApplySteps(state->transform_steps);
 }
 
-TVM_REGISTER_GLOBAL("auto_schedule.TuningOptions")
+TVM_REGISTER_GLOBAL("auto_scheduler.TuningOptions")
     .set_body_typed([](int num_measure_trials, int early_stopping, int num_measures_per_round,
                        int verbose, ProgramBuilder builder, ProgramRunner runner,
                        Optional<Array<MeasureCallback>> measure_callbacks,
@@ -70,12 +72,12 @@ TVM_REGISTER_GLOBAL("auto_schedule.TuningOptions")
                            builder, runner, measure_callbacks, pre_search_callbacks);
     });
 
-TVM_REGISTER_GLOBAL("auto_schedule.AutoSchedule")
+TVM_REGISTER_GLOBAL("auto_scheduler.AutoSchedule")
     .set_body_typed([](SearchTask task, SearchPolicy search_policy, TuningOptions tuning_options) {
       te::Schedule sch;
       Array<te::Tensor> return_tensors;
       std::tie(sch, return_tensors) = AutoSchedule(task, search_policy, tuning_options);
       return Array<ObjectRef>{sch, return_tensors};
     });
-}  // namespace auto_schedule
+}  // namespace auto_scheduler
 }  // namespace tvm

src/auto_schedule/auto_schedule.h → src/auto_scheduler/auto_schedule.h

@@ -18,22 +18,22 @@
  */
 
 /*!
- * \file auto_schedule/auto_schedule.h
+ * \file auto_scheduler/auto_schedule.h
  * \brief The user interface of the TVM Auto-scheduler. This is the entry structure to get
  * schedule search requirements from upper level (Python API), and returns a high performance
  * schedule after search process.
  */
 
-#ifndef TVM_AUTO_SCHEDULE_AUTO_SCHEDULE_H_
-#define TVM_AUTO_SCHEDULE_AUTO_SCHEDULE_H_
+#ifndef TVM_AUTO_SCHEDULER_AUTO_SCHEDULE_H_
+#define TVM_AUTO_SCHEDULER_AUTO_SCHEDULE_H_
 
 #include <utility>
 
 #include "measure.h"
 #include "search_policy/search_policy.h"
 
 namespace tvm {
-namespace auto_schedule {
+namespace auto_scheduler {
 
 /*! \brief Tuning and measurement options. */
 class TuningOptionsNode : public Object {
@@ -69,7 +69,7 @@ class TuningOptionsNode : public Object {
     v->Visit("pre_search_callbacks", &pre_search_callbacks);
   }
 
-  static constexpr const char* _type_key = "auto_schedule.TuningOptions";
+  static constexpr const char* _type_key = "auto_scheduler.TuningOptions";
   TVM_DECLARE_FINAL_OBJECT_INFO(TuningOptionsNode, Object);
 };
 
@@ -110,7 +110,7 @@ class TuningOptions : public ObjectRef {
 TVM_DLL std::pair<te::Schedule, Array<te::Tensor>> AutoSchedule(SearchTask task,
                                                                 SearchPolicy search_policy,
                                                                 TuningOptions tuning_options);
-}  // namespace auto_schedule
+}  // namespace auto_scheduler
 }  // namespace tvm
 
-#endif  // TVM_AUTO_SCHEDULE_AUTO_SCHEDULE_H_
+#endif  // TVM_AUTO_SCHEDULER_AUTO_SCHEDULE_H_