chore: import upstream snapshot with attribution

tests/python/relax/test_codegen_dnnl.py

@@ -0,0 +1,96 @@
+# 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.
+import numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+from tvm import relax
+from tvm.contrib.pickle_memoize import memoize
+from tvm.relax.dpl import make_fused_bias_activation_pattern
+from tvm.script import relax as R
+
+
+@tvm.script.ir_module
+class Conv2dReLUx2:
+    @R.function
+    def main(
+        data: R.Tensor((1, 64, 56, 56), "float32"),
+        weight1: R.Tensor((64, 64, 3, 3), "float32"),
+        weight2: R.Tensor((64, 64, 3, 3), "float32"),
+    ):
+        with R.dataflow():
+            conv1 = relax.op.nn.relu(relax.op.nn.conv2d(data, weight1, padding=(1, 1)))
+            conv2 = relax.op.nn.relu(relax.op.nn.conv2d(conv1, weight2, padding=(0, 0)))
+            R.output(conv2)
+
+        return conv2
+
+
+has_dnnl = tvm.get_global_func("relax.ext.dnnl", True)
+
+dnnl_enabled = pytest.mark.skipif(
+    not has_dnnl,
+    reason="DNNL note enabled.",
+)
+
+pytestmark = [dnnl_enabled]
+
+
+def build_and_run(mod, inputs, legalize=False):
+    target = tvm.target.Target("llvm")
+    dev = tvm.cpu()
+    inputs = [tvm.runtime.tensor(inp, dev) for inp in inputs]
+
+    with tvm.transform.PassContext(config={"relax.transform.apply_legalize_ops": legalize}):
+        ex = tvm.compile(mod, target)
+    vm = relax.VirtualMachine(ex, dev)
+    f = vm["main"]
+    return f(*inputs).numpy()
+
+
+def test_dnnl_offload():
+    pat = make_fused_bias_activation_pattern(
+        "relax.nn.conv2d", with_bias=False, activation="relax.nn.relu"
+    )
+
+    seq = tvm.transform.Sequential(
+        [
+            relax.transform.FuseOpsByPattern([("dnnl.conv2d_relu", pat)]),
+            relax.transform.MergeCompositeFunctions(),
+            relax.transform.RunCodegen(),
+        ]
+    )
+
+    @memoize("relax.tests.test_codegen_dnnl.conv2d_relu_x2")
+    def get_ref():
+        data_np = np.random.randn(1, 64, 56, 56).astype("float32")
+        weight1_np = np.random.randn(64, 64, 3, 3).astype("float32")
+        weight2_np = np.random.randn(64, 64, 3, 3).astype("float32")
+        inputs = [data_np, weight1_np, weight2_np]
+        ref = build_and_run(Conv2dReLUx2, inputs, legalize=True)
+        return inputs, ref
+
+    inputs, ref = get_ref()
+
+    out = build_and_run(seq(Conv2dReLUx2), inputs)
+
+    tvm.testing.assert_allclose(out, ref, rtol=1e-3, atol=1e-3)
+
+
+if __name__ == "__main__":
+    test_dnnl_offload()