[AutoScheduler] Add sampling to dispatcher (apache#7376)

* [AutoScheduler] Add sampling to dispatcher

* address comment

* make measurment configurable

python/tvm/auto_scheduler/__init__.py

@@ -33,7 +33,7 @@
 # Shortcut
 from .compute_dag import ComputeDAG, LayoutRewriteOption, get_shape_from_rewritten_layout
 from .cost_model import RandomModel, XGBModel
-from .dispatcher import DispatchContext, ApplyHistoryBest
+from .dispatcher import DispatchContext, ApplyHistoryBest, ApplyHistoryBestOrSample
 from .measure import (
     MeasureInput,
     MeasureResult,

python/tvm/auto_scheduler/dispatcher.py

@@ -28,8 +28,13 @@
 
 import numpy as np
 
+from tvm.contrib.utils import tempdir
 from tvm.tir.expr import FloatImm
-from .measure_record import load_records
+from .cost_model import RandomModel, XGBModel
+from .measure import LocalRPCMeasureContext
+from .measure_record import RecordToFile, load_records
+from .search_policy import PreloadMeasuredStates, SketchPolicy
+from .search_task import SearchTask, TuningOptions
 from .utils import calc_workload_dis_factor, decode_workload_key
 
 logger = logging.getLogger("auto_scheduler")
@@ -301,6 +306,92 @@ def update(self, target, workload_key, state):
             entry[workload_args] = (state, 1)
 
 
+class ApplyHistoryBestOrSample(ApplyHistoryBest):
+    """
+    Apply the history best config, or sample a valid schedule if no config is found.
+
+    Parameters
+    ----------
+    records : str or iterator of (auto_scheduler.measure.MeasureInput,\
+                                  auto_scheduler.measure.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.
+    sample_simple_workloads: bool
+        When False, sampling will not apply to simple workloads (w/o reduction).
+    cost_model_file: str
+        The filename of the pre-trained XGBoost cost model. If not present, then random
+        model will be used.
+    num_measure: int
+        Meausre the top-N rank of sampled schedules on the device. The default -1 means
+        no measurement and simply return the top-1 schedule ranked by the cost model.
+    """
+
+    def __init__(
+        self, records, sample_simple_workloads=False, cost_model_file=None, num_measure=-1
+    ):
+        self.sample_simple_workloads = sample_simple_workloads
+        self.num_measure = num_measure
+        self.log_dir = tempdir()
+        if cost_model_file is None:
+            self.cost_model = RandomModel()
+        else:
+            self.cost_model = XGBModel()
+            self.cost_model.load(cost_model_file)
+
+        super(ApplyHistoryBestOrSample, self).__init__(
+            records, n_lines=None, include_compatible=True
+        )
+
+    def query(self, target, workload_key, has_complex_op, dag):
+        if has_complex_op or self.sample_simple_workloads:
+            ret = self._query_inside(target, workload_key)
+        else:
+            ret = super(ApplyHistoryBestOrSample, self)._query_inside(target, workload_key)
+
+        if ret is None:
+            ret = self._old_ctx.query(target, workload_key, has_complex_op, dag)
+        return ret
+
+    def _query_inside(self, target, workload_key):
+        ret = super(ApplyHistoryBestOrSample, self)._query_inside(target, workload_key)
+        if ret is not None:
+            return ret
+
+        # Sampling valid schedules when no existing records can be used.
+        task = SearchTask(workload_key=workload_key, target=target)
+        measure_ctx = LocalRPCMeasureContext(min_repeat_ms=300)
+
+        log_file = self.log_dir.relpath("%s.log" % decode_workload_key(workload_key)[0])
+
+        while ret is None:
+            tune_option = TuningOptions(
+                num_measure_trials=self.num_measure,
+                runner=measure_ctx.runner,
+                measure_callbacks=[RecordToFile(log_file)],
+                verbose=0,
+            )
+            search_policy = SketchPolicy(
+                task,
+                self.cost_model,
+                params={
+                    "eps_greedy": 0.01,
+                    "sample_init_min_population": 64,
+                    "evolutionary_search_num_iters": 0,
+                },
+                init_search_callbacks=[PreloadMeasuredStates(log_file)],
+                verbose=0,
+            )
+            task.tune(tune_option, search_policy)
+
+            # Load the sampled records and query again.
+            self.load(log_file)
+            ret = super(ApplyHistoryBestOrSample, self)._query_inside(target, workload_key)
+
+        del measure_ctx
+        return ret
+
+
 class FallbackContext(DispatchContext):
     """
     A fallback dispatch context.

tests/python/relay/test_auto_scheduler_tuning.py

@@ -56,9 +56,16 @@ def tune_network(network, target):
             ):
                 lib = relay.build(mod, target=target, params=params)
 
+        # Sample a schedule when missing
+        with auto_scheduler.ApplyHistoryBestOrSample(None, num_measure=2):
+            with tvm.transform.PassContext(
+                opt_level=3, config={"relay.backend.use_auto_scheduler": True}
+            ):
+                lib2 = relay.build(mod, target=target, params=params)
+
         # Compile without auto-scheduler and any other optimization for correctness check
         with tvm.transform.PassContext(opt_level=0):
-            lib2 = relay.build(mod, target=target, params=params)
+            ref_lib = relay.build(mod, target=target, params=params)
 
         # Check the correctness
         def get_output(data, lib):
@@ -76,10 +83,12 @@ def get_output(data, lib):
         else:
             raise ValueError("Unknown network: " + network)
 
-        actual_output = get_output(data, lib)
-        expected_output = get_output(data, lib2)
+        actual_output1 = get_output(data, lib)
+        actual_output2 = get_output(data, lib2)
+        expected_output = get_output(data, ref_lib)
 
-        tvm.testing.assert_allclose(actual_output, expected_output, rtol=1e-4, atol=1e-4)
+        tvm.testing.assert_allclose(actual_output1, expected_output, rtol=1e-4, atol=1e-4)
+        tvm.testing.assert_allclose(actual_output2, expected_output, rtol=1e-4, atol=1e-4)
 
 
 @tvm.testing.requires_cuda