[GitHub] [tvm] mehrdadh commented on a diff in pull request #11395: [Hexagon] Minimal example demonstrating how to tune.

[GitBox <gi...@apache.org>]

mehrdadh commented on code in PR #11395:
URL: https://github.com/apache/tvm/pull/11395#discussion_r878340561


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tests/python/contrib/test_hexagon/test_autotvm.py:
##########
@@ -0,0 +1,148 @@
+# 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.
+
+import contextlib
+import os
+import sys
+import pytest
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import tir, te, TVMError
+from tvm.script import tir as T
+from tvm import autotvm
+
+
+@autotvm.template("demo_template")
+def demo_template():
+    M, N, K = [1024] * 3
+    A = te.placeholder((M, K), dtype="float32")
+    B = te.placeholder((N, K), dtype="float32")
+    k = te.reduce_axis((0, 1024), name="k")
+    C = te.compute((M, N), lambda i, j: te.sum(A[i, k] * B[j, k], axis=[k]))
+
+    s = te.create_schedule(C.op)
+    cfg = autotvm.get_config()
+
+    m_iter, n_iter = s[C].op.axis
+    (k_iter,) = s[C].op.reduce_axis
+
+    cfg.define_split("k_split", k_iter, num_outputs=2)
+    ko, ki = cfg["k_split"].apply(s, C, k_iter)
+
+    return s, [A, B, C]
+
+
+class HexagonModuleLoader:
+    def __init__(self, hexagon_session, pre_load_function=None) -> None:
+        self.pre_load_function = pre_load_function
+        self.hexagon_session = hexagon_session
+
+    @contextlib.contextmanager
+    def __call__(self, remote_kwargs, build_result):
+        remote = self.hexagon_session._rpc
+        if self.pre_load_function is not None:
+            self.pre_load_function(remote, build_result)
+
+        try:
+            yield remote, self.hexagon_session.load_module(build_result)
+        finally:
+            pass
+
+
+def tune_tasks(
+    tasks,
+    measure_option,
+    tuner="xgb",
+    n_trial=2048,
+    early_stopping=None,
+    log_filename="tuning.log",
+    use_transfer_learning=True,
+):
+    from tvm.autotvm.tuner import XGBTuner
+    from tvm.autotvm.tuner import GATuner
+
+    tmp_log_file = log_filename + ".tmp"
+    if os.path.exists(tmp_log_file):
+        os.remove(tmp_log_file)
+
+    for i, tsk in enumerate(reversed(tasks)):
+        prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+        if tuner == "xgb" or tuner == "xgb-rank":
+            tuner_obj = XGBTuner(tsk, loss_type="rank")
+        elif tuner == "xgb_knob":
+            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+        elif tuner == "ga":
+            tuner_obj = GATuner(tsk, pop_size=50)
+        elif tuner == "random":
+            tuner_obj = RandomTuner(tsk)
+        elif tuner == "gridsearch":
+            tuner_obj = GridSearchTuner(tsk)
+        else:
+            raise ValueError("Invalid tuner: " + tuner)
+
+        if use_transfer_learning:
+            if os.path.isfile(tmp_log_file):
+                tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
+
+        tsk_trial = min(n_trial, len(tsk.config_space))
+        tuner_obj.tune(
+            n_trial=tsk_trial,
+            early_stopping=early_stopping,
+            measure_option=measure_option,
+            callbacks=[
+                autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
+                autotvm.callback.log_to_file(tmp_log_file),
+            ],
+        )
+
+    autotvm.record.pick_best(tmp_log_file, log_filename)
+    os.remove(tmp_log_file)
+
+
+@pytest.mark.skip(reason="AutoTVM tuning is not yet enabled on Hexagon")
+@tvm.testing.requires_hexagon
+def test_autotvm(hexagon_session):
+    logfilename = "./hexagon.autotvm.log"
+
+    options = {
+        "log_filename": logfilename,
+        "early_stopping": None,
+        "measure_option": autotvm.measure_option(
+            builder=autotvm.LocalBuilder(timeout=15),
+            runner=autotvm.RPCRunner(
+                module_loader=HexagonModuleLoader(hexagon_session),
+                key=hexagon_session._remote_kw["key"],
+                host=hexagon_session._remote_kw["host"],
+                port=hexagon_session._remote_kw["port"],
+                number=3,
+                timeout=15,
+                min_repeat_ms=150,
+                # cooldown_interval=150

Review Comment:
   nit: remove comment



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