[ANSOR] Auto-scheduler tutorial for GPU and necessary refactor/fix

docs/api/python/auto_scheduler.rst

@@ -31,5 +31,20 @@ tvm.auto_scheduler.auto_schedule
 
 .. autofunction:: tvm.auto_scheduler.auto_schedule.auto_schedule
 
+tvm.auto_scheduler.workload_registry
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. autofunction:: tvm.auto_scheduler.workload_registry.register_workload
 
+
+tvm.auto_scheduler.measure
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.auto_scheduler.measure
+
+.. autoclass:: tvm.auto_scheduler.measure.LocalRPCMeasureContext
+
+.. autoclass:: tvm.auto_scheduler.measure.LocalRunner
+
+.. autoclass:: tvm.auto_scheduler.measure.LocalBuilder
+
+.. autoclass:: tvm.auto_scheduler.measure.RPCRunner

include/tvm/auto_scheduler/search_policy.h

@@ -65,6 +65,7 @@
 #include <tvm/auto_scheduler/search_task.h>
 #include <tvm/node/node.h>
 
+#include <string>
 #include <unordered_set>
 #include <vector>
 
@@ -191,7 +192,7 @@ class SearchPolicyNode : public Object {
    * We store the string format of a state for redundancy check. This is used to make sure a
    * measured state will never be measured again.
    */
-  std::unordered_set<String> measured_states_set_;
+  std::unordered_set<std::string> measured_states_set_;
   /*! \brief The array of already measured states.
    *  The good states can be used as the initial population in evolutionary search. */
   std::vector<State> measured_states_vector_;

python/tvm/auto_scheduler/measure.py

@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-"""
+r"""
 Distributed measurement infrastructure to measure the runtime costs of tensor programs.
 
 These functions are responsible for building the tvm module, uploading it to
@@ -25,8 +25,8 @@
 A builder builds the executable binary files and a runner runs the binary files to
 get the measurement results. The flow of data structures is
 
-                `ProgramBuilder`                 `ProgramRunner`
-`MeasureInput` -----------------> `BuildResult` ----------------> `MeasureResult`
+  .                `ProgramBuilder`                 `ProgramRunner`
+  `MeasureInput` -----------------> `BuildResult` ----------------> `MeasureResult`
 
 We implement these in python to utilize python's multiprocessing and error handling.
 """
@@ -222,7 +222,7 @@ class LocalRunner(ProgramRunner):
         where the first "1" is warm up and will be discarded.
         The returned result contains `repeat` costs,
         each of which is an average of `number` costs.
-    min_repeat_ms : int = 0
+    min_repeat_ms : int = 100
         The minimum duration of one `repeat` in milliseconds.
         By default, one `repeat` contains `number` runs. If this parameter is set,
         the parameters `number` will be dynamically adjusted to meet the
@@ -244,7 +244,7 @@ def __init__(
         timeout=10,
         number=3,
         repeat=1,
-        min_repeat_ms=0,
+        min_repeat_ms=100,
         cooldown_interval=0.0,
         enable_cpu_cache_flush=False,
     ):
@@ -289,7 +289,7 @@ class RPCRunner(ProgramRunner):
         where the first "1" is warm up and will be discarded.
         The returned result contains `repeat` costs,
         each of which is an average of `number` costs.
-    min_repeat_ms : int = 0
+    min_repeat_ms : int = 100
         The minimum duration of one `repeat` in milliseconds.
         By default, one `repeat` contains `number` runs. If this parameter is set,
         the parameters `number` will be dynamically adjusted to meet the
@@ -316,7 +316,7 @@ def __init__(
         timeout=10,
         number=3,
         repeat=1,
-        min_repeat_ms=0,
+        min_repeat_ms=100,
         cooldown_interval=0.0,
         enable_cpu_cache_flush=False,
     ):

python/tvm/auto_scheduler/search_policy.py

@@ -91,7 +91,7 @@ class SketchPolicy(SearchPolicy):
     ----------
     task : SearchTask
         The SearchTask for the computation declaration.
-    schedule_cost_model : CostModel = RandomModel()
+    program_cost_model : CostModel = RandomModel()
         The cost model to estimate the complete schedules.
     params : Optional[Dict[str, Any]]
         Parameters of the search policy.
@@ -129,7 +129,7 @@ class SketchPolicy(SearchPolicy):
     def __init__(
         self,
         task,
-        schedule_cost_model=RandomModel(),
+        program_cost_model=RandomModel(),
         params=None,
         seed=None,
         verbose=1,
@@ -145,7 +145,7 @@ def __init__(
         self.__init_handle_by_constructor__(
             _ffi_api.SketchPolicy,
             task,
-            schedule_cost_model,
+            program_cost_model,
             params,
             seed or random.randint(1, 1 << 30),
             verbose,

python/tvm/auto_scheduler/workload_registry.py

@@ -55,13 +55,15 @@ def register_workload(func_name, f=None, override=False):
 
     Examples
     --------
-    @auto_scheduler.register_workload
-    def matmul(N, M, K):
-        A = te.placeholder((N, K), name='A')
-        B = te.placeholder((K, M), name='B')
-        k = te.reduce_axis((0, K), name='k')
-        C = te.compute((N, M), lambda i, j: tvm.sum(A[i][k] * B[k][j], axis=[k]), name='C')
-        return [A, B, C]
+    .. code-block:: python
+
+      @auto_scheduler.register_workload
+      def matmul(N, M, K):
+          A = te.placeholder((N, K), name='A')
+          B = te.placeholder((K, M), name='B')
+          k = te.reduce_axis((0, K), name='k')
+          C = te.compute((N, M), lambda i, j: tvm.sum(A[i][k] * B[k][j], axis=[k]), name='C')
+          return [A, B, C]
     """
     global WORKLOAD_FUNC_REGISTRY
 

python/tvm/micro/session.py

@@ -22,10 +22,14 @@
 
 from .._ffi import get_global_func
 from ..contrib import graph_runtime
-from .base import _rpc_connect
 from ..rpc import RPCSession
 from .transport import TransportLogger
 
+try:
+    from .base import _rpc_connect
+except ImportError:
+    raise ImportError("micro tvm is not enabled. Set USE_MICRO to ON in config.cmake")
+
 
 class Session:
     """MicroTVM Device Session