[GitHub] [tvm] csullivan commented on a diff in pull request #11549: [HEXAGON] Initial clip operator for Hexagon

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

csullivan commented on code in PR #11549:
URL: https://github.com/apache/tvm/pull/11549#discussion_r906178546


##########
tests/python/contrib/test_hexagon/topi/test_clip.py:
##########
@@ -0,0 +1,127 @@
+# 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.
+
+# pylint: disable=invalid-name
+
+import pytest
+import numpy as np
+
+from tvm import te, topi
+
+import tvm.testing
+from tvm.topi import testing
+from tvm.contrib.hexagon.build import HexagonLauncher
+import tvm.topi.hexagon.slice_ops as sl
+from ..infrastructure import allocate_hexagon_array, transform_numpy
+
+input_layout = tvm.testing.parameter(
+    "nhwc-8h2w32c2w-2d",
+)
+
+
+@tvm.testing.fixture
+def input_np(input_shape, dtype):
+    return np.random.random(input_shape).astype(dtype)
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, output_layout):
+    return transform_numpy(expected_output_np, "nhwc", output_layout)
+
+
+@tvm.testing.fixture
+def transformed_input_np(input_np, input_layout):
+    return transform_numpy(input_np, "nhwc", input_layout)
+
+
+class TestClipSlice:
+    input_shape, output_shape, A_min, A_max, output_layout, dtype = tvm.testing.parameters(
+        ([1, 8, 4, 32], [1, 8, 4, 32], 0.1, 0.5, "nhwc-8h2w32c2w-2d", "float16")
+    )
+
+    @tvm.testing.fixture
+    def expected_output_np(self, input_np, A_min, A_max):
+        ref_np = np.clip(input_np, A_min, A_max)
+        return ref_np
+
+    @tvm.testing.requires_hexagon
+    def test_clip_slice(
+        self,
+        input_shape,
+        output_shape,
+        input_np,
+        input_layout,
+        output_layout,
+        dtype,
+        A_min,
+        A_max,
+        transformed_input_np,
+        transformed_expected_output_np,
+        hexagon_session,
+    ):
+        # establish target and input placeholder
+        target_hexagon = tvm.target.hexagon("v68")
+        A = te.placeholder(input_shape, name="A", dtype=dtype)
+
+        # get the compute function and schedule
+        M = sl.clip_compute(A, A_min, A_max)
+
+        # Assume layout is nhwc-8h2w32c2w-2d
+        tir_schedule = sl.clip_schedule(M, A, output_layout, input_layout)
+        sch = tir_schedule.mod
+
+        # build the function
+        with tvm.transform.PassContext(opt_level=3):
+            func = tvm.build(
+                sch, [A, M], tvm.target.Target(target_hexagon, host=target_hexagon), name="clip"

Review Comment:
   Hi @jcoplin-quic, with TIR schedules you'll want to pass in the module, sch.mod, in which case you don't need the tensor list as @Lunderberg pointed out.



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