[Hexagon] Add contrib tests for blocked conv2d and maxpool2d

tests/python/contrib/test_hexagon/__init__.py

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+# 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.
+
+""" Testing infrastructure for Hexagon """

tests/python/contrib/test_hexagon/conftest.py

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+# 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.
+
+""" Hexagon testing fixtures used to deduce testing argument
+    values from testing parameters """
+
+import tvm
+from .infrastructure import get_packed_filter_layout
+
+
+@tvm.testing.fixture
+def shape_nhwc(batch, in_channel, in_size):
+    return (batch, in_size, in_size, in_channel)
+
+
+@tvm.testing.fixture
+def shape_oihw(out_channel, in_channel, kernel):
+    return (out_channel, in_channel, kernel, kernel)
+
+
+@tvm.testing.fixture
+def shape_oihw8i32o4i(out_channel, in_channel, kernel):
+    return get_packed_filter_layout(out_channel, in_channel, kernel, kernel)

tests/python/contrib/test_hexagon/infrastructure.py

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+# 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.
+
+""" Hexagon testing infrastructure """
+
+import tvm
+import numpy
+
+
+def ceildiv(o, d):
+    return tvm.tir.floordiv(o + d - 1, d)
+
+
+def get_packed_activation_layout(shape_nhwc, block_shape, packed_C=True):
+    assert len(shape_nhwc) == 4
+    shape = [shape_nhwc[0]]
+    off_h, off_w, off_c = block_shape
+    shape.append(ceildiv(shape_nhwc[1], off_h))
+    shape.append(ceildiv(shape_nhwc[2], off_w))
+    if packed_C:
+        shape.append(ceildiv(shape_nhwc[3], off_c))
+        shape.extend(block_shape)
+    else:
+        shape.extend([off_h, off_w, shape_nhwc[3]])
+    return shape
+
+
+def get_packed_filter_layout(out_channel, in_channel, kernel_h, kernel_w):
+    out_factor, in_first_factor, in_second_factor = 32, 32, 4
+    return (
+        int(ceildiv(out_channel, out_factor)),
+        int(ceildiv(in_channel, in_first_factor)),
+        kernel_h,
+        kernel_w,
+        in_first_factor // in_second_factor,
+        out_factor,
+        in_second_factor,
+    )
+
+
+def build_and_run(inputs, func, target, target_host, *args, **kwargs):
+    schedule, placeholders, binds = func(*args, **kwargs)
+
+    func = tvm.build(schedule, placeholders, target=target, target_host=target_host, binds=binds)
+    dev = tvm.device(target)
+    tensors = []
+    for tensor in inputs:
+        tensors.append(tvm.nd.array(tensor, dev))
+    tensors.append(
+        tvm.nd.array(
+            numpy.zeros([i.value for i in placeholders[-1].shape], dtype=placeholders[-1].dtype),
+            dev,
+        )
+    )
+    func(*tensors)
+
+    return tensors[-1].asnumpy()
+
+
+def get_block_shape():
+    return 8, 8, 32
+
+
+def get_conv2d_nhwc_shape(shape_nhwc, kernel_size, strides, padding, dilation, out_channels):
+    assert len(shape_nhwc) == 4
+    kernel = []
+    kernel.append((kernel_size[0] - 1) * dilation[0] + 1)
+    kernel.append((kernel_size[1] - 1) * dilation[1] + 1)
+    return (
+        shape_nhwc[0],
+        (shape_nhwc[1] - kernel[0] + padding[0] + padding[1]) // strides[0] + 1,
+        (shape_nhwc[2] - kernel[1] + padding[2] + padding[3]) // strides[1] + 1,
+        out_channels,
+    )