wehub-resource-sync
793 lines
29 KiB
Python
793 lines
29 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import pytest
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from utils import skip_unless_adreno_opencl_vulkan, verify_results
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import tvm
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import tvm.testing
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from tvm.script.parser import ir as I
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from tvm.script.parser import relax as R
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TARGETS = [
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tvm.target.Target("qcom/adreno-opencl-texture"),
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# tvm.target.Target("qcom/adreno-vulkan-texture"),
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]
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ref_target = tvm.target.Target("llvm")
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 64, 56, 56), "float32"), w: R.Tensor((32, 64, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 32, 54, 54), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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R.output(gv)
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return gv
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_relu():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv)
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_relu_conv2d_relu():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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x0: R.Tensor((2, 16, 28, 28), "float32") = R.nn.relu(x)
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x0, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv)
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_relu_tanh():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv)
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gv3: R.Tensor((2, 4, 26, 26), "float32") = R.tanh(gv2)
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R.output(gv3)
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return gv3
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_add():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"),
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w: R.Tensor((4, 16, 3, 3), "float32"),
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bias: R.Tensor((2, 4, 26, 26), "float32"),
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, bias)
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_sum():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=2):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4), "float32") = R.sum(gv, axis=[2, 3])
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_sum_keepdims():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=2):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 1, 1), "float32") = R.sum(gv, axis=[2, 3], keepdims=True)
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_sum_reduce():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=2):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 26), "float32") = R.sum(gv, axis=[1, 2])
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_transpose():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((26, 26, 4, 2), "float32") = R.permute_dims(gv, axes=[3, 2, 1, 0])
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_expand_dims():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=6):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 1, 4, 1, 26, 26), "float32") = R.expand_dims(gv, axis=(-3, 1))
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_squeeze():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((1, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=3):
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with R.dataflow():
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gv: R.Tensor((1, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((4, 26, 26), "float32") = R.squeeze(gv, axis=[0])
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_strided_slice():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 2, 9, 7), dtype="float32") = R.strided_slice(
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gv, begin=[0, 0, 0], end=[4, 26, 26], strides=[2, 3, 4], axes=[1, 2, 3]
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)
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_relu_concat():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv)
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gv3: R.Tensor((2, 8, 26, 26), "float32") = R.concat((gv, gv2), axis=1)
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R.output(gv3)
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return gv3
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_relu_concat_split():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv)
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gv3: R.Tensor((2, 8, 26, 26), "float32") = R.concat((gv, gv2), axis=1)
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gv4 = R.split(gv3, indices_or_sections=2, axis=1)
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# TODO @Siva: Multi value return have an issue at runtime.
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gv5 = gv4[0]
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R.output(gv5)
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return gv5
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_relu_concat_split_transpose_concat():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2: R.Tensor((2, 4, 26, 26), "float32") = R.nn.relu(gv)
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gv3: R.Tensor((2, 8, 26, 26), "float32") = R.concat((gv, gv2), axis=1)
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gv4 = R.split(gv3, indices_or_sections=2, axis=1)
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gv5: R.Tensor((26, 26, 4, 2), "float32") = R.permute_dims(gv4[0], axes=[3, 2, 1, 0])
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gv6: R.Tensor((26, 26, 4, 2), "float32") = R.permute_dims(gv4[1], axes=[3, 2, 1, 0])
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gv7: R.Tensor((26, 26, 8, 2), "float32") = R.concat((gv5, gv6), axis=2)
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R.output(gv7)
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return gv7
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verify_results(Input, target, ref_target)
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@pytest.mark.skip(reason="Known failure: numerical mismatch in texture lowering")
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_maxpool2d():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2 = R.nn.max_pool2d(
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gv,
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pool_size=[2, 2],
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strides=[2, 2],
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padding=[0, 0],
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layout="NCHW",
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out_layout="NCHW",
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)
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.skip(reason="Known failure: numerical mismatch in texture lowering")
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_avgpool2d():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
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) -> R.Tensor(None, "float32", ndim=4):
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with R.dataflow():
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gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
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gv2 = R.nn.adaptive_avg_pool2d(gv, output_size=[13, 13], layout="NCHW")
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R.output(gv2)
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return gv2
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verify_results(Input, target, ref_target)
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@pytest.mark.gpu
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@skip_unless_adreno_opencl_vulkan
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@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
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def test_conv2d_softmax():
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target = TARGETS[0]
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@I.ir_module
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class Input:
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@R.function
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def main(
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x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
|
|
gv2 = R.nn.softmax(gv, axis=1)
|
|
R.output(gv2)
|
|
return gv2
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_conv2d_layernorm():
|
|
target = TARGETS[0]
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 16, 28, 28), "float32"),
|
|
w: R.Tensor((4, 16, 3, 3), "float32"),
|
|
gamma: R.Tensor((26, 26), dtype="float32"),
|
|
beta: R.Tensor((26, 26), dtype="float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
|
|
gv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.nn.layer_norm(
|
|
gv, gamma, beta, axes=[-2, -1]
|
|
)
|
|
R.output(gv2)
|
|
return gv2
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_binary_broadcast():
|
|
target = TARGETS[0]
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 16, 28, 28), "float32"),
|
|
w: R.Tensor((4, 16, 3, 3), "float32"),
|
|
bias: R.Tensor((26, 26), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
|
|
gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, bias)
|
|
R.output(gv2)
|
|
return gv2
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_binary_ewise_scalar():
|
|
target = TARGETS[0]
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 16, 28, 28), "float32"), w: R.Tensor((4, 16, 3, 3), "float32")
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
|
|
gv2: R.Tensor((2, 4, 26, 26), "float32") = R.add(gv, R.const(1, "float32"))
|
|
R.output(gv2)
|
|
return gv2
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_residual_block():
|
|
target = TARGETS[0]
|
|
r"""
|
|
- some kind of residual block followed by convolution to have texture after residual block
|
|
- scalar data type verification which should be mapped to global memory scope
|
|
layout_transform (NCHW->NCHW4c)
|
|
| <- buffer
|
|
conv2d (1) <- to get textures as output
|
|
/ \
|
|
conv2d (2) |
|
|
\ /
|
|
add <- add should be fused into conv2d (2)
|
|
multiply to scalar <- buffer to the input of multiply scalar value
|
|
relu
|
|
| <- texture in intermediate tensor
|
|
conv2d (3)
|
|
relu
|
|
| <- buffer
|
|
layout_transform (NCHW4c->NCHW)
|
|
"""
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 32, 40, 40), "float32"),
|
|
w1: R.Tensor((32, 32, 2, 2), "float32"),
|
|
w2: R.Tensor((32, 32, 1, 1), "float32"),
|
|
w3: R.Tensor((32, 32, 2, 2), "float32"),
|
|
bias: R.Tensor((1, 32, 1, 1), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv = R.nn.conv2d(x, w1, strides=[2, 2], out_dtype="float32")
|
|
gv1 = R.add(gv, bias)
|
|
gv2 = R.nn.relu(gv1)
|
|
gv3 = R.nn.conv2d(gv2, w2, strides=[1, 1], out_dtype="float32")
|
|
bias_1 = R.multiply(bias, R.const(0.15, "float32"))
|
|
gv4 = R.add(gv3, bias_1)
|
|
gv5 = R.nn.relu(gv4)
|
|
gv6 = R.nn.conv2d(gv5, w3, strides=[2, 2], out_dtype="float32")
|
|
gv7 = R.nn.relu(gv6)
|
|
R.output(gv7)
|
|
return gv7
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_conv2d_conv2d_fallback_to_buffer_conv2d():
|
|
target = TARGETS[0]
|
|
r"""
|
|
layout_transform (NCHW->NCHW4c)
|
|
| <- texture
|
|
conv2d (1) <- textures as output
|
|
/ \
|
|
conv2d (2) conv2d (3) <- conv2d (2) emits texture, conv2d (3) emits buffer
|
|
\ / <- concat shouldn't support textures here
|
|
concatenation
|
|
| <- buffer
|
|
layout_transform (NCHW4c->NCHW)
|
|
"""
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 32, 40, 40), "float32"),
|
|
w1: R.Tensor((96, 32, 2, 2), "float32"),
|
|
w2: R.Tensor((32, 96, 2, 2), "float32"),
|
|
w3: R.Tensor((5, 96, 2, 2), "float32"),
|
|
bias1: R.Tensor((1, 96, 1, 1), "float32"),
|
|
bias2: R.Tensor((1, 32, 1, 1), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv = R.nn.conv2d(x, w1, strides=[2, 2], out_dtype="float32")
|
|
gv1 = R.add(gv, bias1)
|
|
gv2 = R.nn.relu(gv1)
|
|
gv3 = R.nn.conv2d(gv2, w2, strides=[2, 2], out_dtype="float32")
|
|
gv6 = R.nn.conv2d(gv2, w3, strides=[2, 2], out_dtype="float32")
|
|
gv7 = R.concat((gv3, gv6), axis=1)
|
|
R.output(gv7)
|
|
return gv7
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_conv2d_conv2d_conv2d_concat():
|
|
target = TARGETS[0]
|
|
r"""
|
|
layout_transform (NCHW->NCHW4c)
|
|
| <- texture
|
|
conv2d (1) <- textures as output
|
|
/ \
|
|
conv2d (2) conv2d (3)
|
|
\ / <- concat does support textures here
|
|
concatenation
|
|
| <- buffer
|
|
layout_transform (NCHW4c->NCHW)
|
|
"""
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 32, 40, 40), "float32"),
|
|
w1: R.Tensor((96, 32, 2, 2), "float32"),
|
|
w2: R.Tensor((32, 96, 2, 2), "float32"),
|
|
w3: R.Tensor((8, 96, 2, 2), "float32"),
|
|
bias1: R.Tensor((1, 96, 1, 1), "float32"),
|
|
bias2: R.Tensor((1, 32, 1, 1), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv = R.nn.conv2d(x, w1, strides=[2, 2], out_dtype="float32")
|
|
gv1 = R.add(gv, bias1)
|
|
gv2 = R.nn.relu(gv1)
|
|
gv3 = R.nn.conv2d(gv2, w2, strides=[2, 2], out_dtype="float32")
|
|
gv6 = R.nn.conv2d(gv2, w3, strides=[2, 2], out_dtype="float32")
|
|
gv7 = R.concat((gv3, gv6), axis=1)
|
|
R.output(gv7)
|
|
return gv7
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.skip(reason="Known failure: numerical mismatch in texture lowering")
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_pooling_branching_texture_params():
|
|
target = TARGETS[0]
|
|
r"""
|
|
Verification of the pooling and many branches having textures
|
|
layout_transform (NCHW->NCHW4c)
|
|
| <- texture
|
|
conv2d (0) <- to get textures
|
|
| <- textures
|
|
pooling
|
|
/ \ \ <- textures
|
|
conv2d (1) conv2d (2) conv2d (3)
|
|
\ / |
|
|
add | <- to have the only one output, will be fused
|
|
\ /
|
|
add <- to have the only one output, will be fused
|
|
| <- buffer
|
|
layout_transform (NCHW4c->NCHW)
|
|
"""
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((2, 32, 40, 40), "float32"),
|
|
w1: R.Tensor((32, 32, 1, 1), "float32"),
|
|
w2: R.Tensor((32, 32, 2, 2), "float32"),
|
|
w3: R.Tensor((32, 32, 1, 1), "float32"),
|
|
w4: R.Tensor((32, 32, 2, 2), "float32"),
|
|
bias1: R.Tensor((1, 32, 1, 1), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
gv = R.nn.conv2d(x, w1, strides=[1, 1], out_dtype="float32")
|
|
gv1 = R.nn.max_pool2d(gv, pool_size=[2, 2], strides=[2, 2])
|
|
gv2 = R.nn.conv2d(
|
|
gv1, w2, padding=[0, 0, 1, 1], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
gv5 = R.nn.conv2d(
|
|
gv1, w3, padding=[0, 0, 0, 0], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
gv6 = R.nn.conv2d(
|
|
gv1, w4, padding=[0, 1, 1, 0], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
gv8 = R.add(gv2, gv5)
|
|
gv9 = R.add(gv8, gv6)
|
|
R.output(gv9)
|
|
return gv9
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_injective_inputs1():
|
|
target = TARGETS[0]
|
|
r"""
|
|
Input
|
|
/ \
|
|
/ |
|
|
| /
|
|
conv2d (1) /
|
|
| /
|
|
conv2d (2) mean
|
|
/ \ /
|
|
| | \ /
|
|
| | (3) add
|
|
| | |
|
|
| \ /
|
|
\ mul
|
|
\ /
|
|
add
|
|
|
|
"""
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((1, 4, 40, 40), "float32"),
|
|
w1: R.Tensor((4, 4, 3, 3), "float32"),
|
|
w2: R.Tensor((4, 4, 3, 3), "float32"),
|
|
w3: R.Tensor((4, 4, 3, 3), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
mean = R.mean(x, axis=1, keepdims=True)
|
|
conv1 = R.nn.conv2d(
|
|
x, w1, padding=[1, 1, 1, 1], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
conv2 = R.nn.conv2d(
|
|
conv1, w2, padding=[1, 1, 1, 1], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
ad3 = R.add(conv1, conv2)
|
|
ad1 = R.add(mean, conv1)
|
|
ad2 = R.multiply(ad1, conv2)
|
|
gv = R.add(ad3, ad2)
|
|
R.output(gv)
|
|
return gv
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@skip_unless_adreno_opencl_vulkan
|
|
@pytest.mark.skipif(not tvm.testing.device_enabled(TARGETS[0]), reason="opencl not enabled")
|
|
def test_injective_nwo_inputs2():
|
|
target = TARGETS[0]
|
|
r"""
|
|
Input
|
|
/ \
|
|
| \
|
|
conv2d \
|
|
| /
|
|
conv2d mean /
|
|
/ \ /
|
|
add | \ |
|
|
| | \ |
|
|
| | \ /
|
|
| | (3) add
|
|
| | |
|
|
| \ /
|
|
| \ /
|
|
\ mul
|
|
\ /
|
|
add
|
|
|
|
"""
|
|
|
|
@I.ir_module
|
|
class Input:
|
|
@R.function
|
|
def main(
|
|
x: R.Tensor((1, 4, 40, 40), "float32"),
|
|
w1: R.Tensor((4, 4, 3, 3), "float32"),
|
|
w2: R.Tensor((4, 4, 3, 3), "float32"),
|
|
w3: R.Tensor((4, 4, 3, 3), "float32"),
|
|
) -> R.Tensor(None, "float32", ndim=4):
|
|
with R.dataflow():
|
|
mean = R.mean(x, axis=1, keepdims=True)
|
|
conv1 = R.nn.conv2d(
|
|
x, w1, padding=[1, 1, 1, 1], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
conv2 = R.nn.conv2d(
|
|
conv1, w2, padding=[1, 1, 1, 1], strides=[1, 1], out_dtype="float32"
|
|
)
|
|
ad3 = R.add(conv1, conv2)
|
|
ad1 = R.add(mean, conv1)
|
|
ad2 = R.multiply(ad1, conv2)
|
|
gv = R.add(ad2, ad3)
|
|
R.output(gv)
|
|
return gv
|
|
|
|
verify_results(Input, target, ref_target)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tvm.testing.main()
|