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[Hexagon] Add contrib tests for blocked conv2d and maxpool2d (apache#…
…8960) * Add hexagon contrib tests for blocked conv2d and maxpool2d * Restructure based on review comments
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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. | ||
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""" Testing infrastructure for Hexagon """ |
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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. | ||
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""" Hexagon testing fixtures used to deduce testing argument | ||
values from testing parameters """ | ||
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import tvm | ||
from .infrastructure import get_packed_filter_layout | ||
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@tvm.testing.fixture | ||
def shape_nhwc(batch, in_channel, in_size): | ||
return (batch, in_size, in_size, in_channel) | ||
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@tvm.testing.fixture | ||
def shape_oihw(out_channel, in_channel, kernel): | ||
return (out_channel, in_channel, kernel, kernel) | ||
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@tvm.testing.fixture | ||
def shape_oihw8i32o4i(out_channel, in_channel, kernel): | ||
return get_packed_filter_layout(out_channel, in_channel, kernel, kernel) |
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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. | ||
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""" Hexagon testing infrastructure """ | ||
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import tvm | ||
import numpy | ||
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def ceildiv(o, d): | ||
return tvm.tir.floordiv(o + d - 1, d) | ||
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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 | ||
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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, | ||
) | ||
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def build_and_run(inputs, func, target, target_host, *args, **kwargs): | ||
schedule, placeholders, binds = func(*args, **kwargs) | ||
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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) | ||
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return tensors[-1].asnumpy() | ||
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def get_block_shape(): | ||
return 8, 8, 32 | ||
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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, | ||
) |
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