[GitHub] [tvm] mehrdadh commented on a diff in pull request #13514: [microTVM] tuning on micro targets with meta-schedule

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

mehrdadh commented on code in PR #13514:
URL: https://github.com/apache/tvm/pull/13514#discussion_r1061753588


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python/tvm/contrib/micro/meta_schedule/test_autotune_ms.py:
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@@ -0,0 +1,181 @@
+# 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 numpy as np
+import pytest
+from types import MappingProxyType
+import pathlib
+import json
+
+import tvm
+from tvm import relay
+from tvm.relay.backend import Executor
+from tvm.contrib import graph_executor, utils
+from tvm import meta_schedule as ms
+from tvm.contrib.micro.meta_schedule.local_builder_micro import get_micro_local_builder
+from tvm.contrib.micro.meta_schedule.rpc_runner_micro import get_rpc_runner_micro
+
+
+def get_module():
+    data_shape = (1, 3, 16, 16)
+    weight_shape = (8, 3, 5, 5)
+    data = relay.var("data", relay.TensorType(data_shape, "float32"))
+    weight = relay.var("weight", relay.TensorType(weight_shape, "float32"))
+    y = relay.nn.conv2d(
+        data,
+        weight,
+        padding=(2, 2),
+        kernel_size=(5, 5),
+        kernel_layout="OIHW",
+        out_dtype="float32",
+    )
+    f = relay.Function([data, weight], y)
+    mod = tvm.IRModule.from_expr(f)
+    mod = relay.transform.InferType()(mod)
+
+    weight_sample = np.random.rand(
+        weight_shape[0], weight_shape[1], weight_shape[2], weight_shape[3]
+    ).astype("float32")
+    params = {mod["main"].params[1].name_hint: weight_sample}
+
+    model_info = {
+        "in_tensor": "data",
+        "in_shape": data_shape,
+        "in_dtype": "float32",
+    }
+
+    return mod, params, model_info
+
+
+@tvm.testing.requires_micro
+@pytest.mark.parametrize(
+    "platform, options",
+    [
+        pytest.param("crt", None),
+        pytest.param(
+            "zephyr",
+            {
+                "board": "qemu_x86",
+                "project_type": "host_driven",
+            },
+        ),
+    ],
+)
+def test_micro_tuning_with_meta_schedule(platform, options):
+    if platform == "crt":
+        target = tvm.target.target.micro(model="host")
+    else:
+        boards_file = (
+            pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")) / "boards.json"
+        )
+        with open(boards_file) as f:
+            boards = json.load(f)
+        target = tvm.target.target.micro(
+            model=boards[options["board"]]["model"], options="-mcpu=cortex-m4"

Review Comment:
   why `-mcpu=cortex-m4` is fixed here?



##########
python/tvm/contrib/micro/meta_schedule/test_autotune_ms.py:
##########
@@ -0,0 +1,181 @@
+# Licensed to the Apache Software Foundation (ASF) under one

Review Comment:
   You need to move this file outside of the python package. Usually we have CRT test and Zephyr/Arduino tests in separate path. CRT test would be in the python unittests and platform specific tests are in `tests/micro`



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