wehub-resource-sync
97 lines
3.0 KiB
Python
97 lines
3.0 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 numpy as np
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import pytest
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import tvm
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import tvm.testing
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from tvm import relax
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from tvm.contrib.pickle_memoize import memoize
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from tvm.relax.dpl import make_fused_bias_activation_pattern
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from tvm.script import relax as R
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@tvm.script.ir_module
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class Conv2dReLUx2:
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@R.function
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def main(
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data: R.Tensor((1, 64, 56, 56), "float32"),
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weight1: R.Tensor((64, 64, 3, 3), "float32"),
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weight2: R.Tensor((64, 64, 3, 3), "float32"),
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):
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with R.dataflow():
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conv1 = relax.op.nn.relu(relax.op.nn.conv2d(data, weight1, padding=(1, 1)))
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conv2 = relax.op.nn.relu(relax.op.nn.conv2d(conv1, weight2, padding=(0, 0)))
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R.output(conv2)
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return conv2
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has_dnnl = tvm.get_global_func("relax.ext.dnnl", True)
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dnnl_enabled = pytest.mark.skipif(
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not has_dnnl,
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reason="DNNL note enabled.",
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)
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pytestmark = [dnnl_enabled]
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def build_and_run(mod, inputs, legalize=False):
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target = tvm.target.Target("llvm")
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dev = tvm.cpu()
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inputs = [tvm.runtime.tensor(inp, dev) for inp in inputs]
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with tvm.transform.PassContext(config={"relax.transform.apply_legalize_ops": legalize}):
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ex = tvm.compile(mod, target)
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vm = relax.VirtualMachine(ex, dev)
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f = vm["main"]
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return f(*inputs).numpy()
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def test_dnnl_offload():
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pat = make_fused_bias_activation_pattern(
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"relax.nn.conv2d", with_bias=False, activation="relax.nn.relu"
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)
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seq = tvm.transform.Sequential(
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[
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relax.transform.FuseOpsByPattern([("dnnl.conv2d_relu", pat)]),
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relax.transform.MergeCompositeFunctions(),
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relax.transform.RunCodegen(),
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]
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)
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@memoize("relax.tests.test_codegen_dnnl.conv2d_relu_x2")
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def get_ref():
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data_np = np.random.randn(1, 64, 56, 56).astype("float32")
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weight1_np = np.random.randn(64, 64, 3, 3).astype("float32")
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weight2_np = np.random.randn(64, 64, 3, 3).astype("float32")
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inputs = [data_np, weight1_np, weight2_np]
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ref = build_and_run(Conv2dReLUx2, inputs, legalize=True)
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return inputs, ref
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inputs, ref = get_ref()
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out = build_and_run(seq(Conv2dReLUx2), inputs)
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tvm.testing.assert_allclose(out, ref, rtol=1e-3, atol=1e-3)
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if __name__ == "__main__":
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test_dnnl_offload()
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