chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,138 @@
+# 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.
+"""NNAPI network tests."""
+
+import numpy as np
+import pytest
+
+pytest.importorskip("onnx")
+
+import onnx
+
+import tvm
+from test_nnapi.conftest import remote
+from test_nnapi.infrastructure import build_and_run  # , build_and_run_vm
+from tvm.contrib.download import download_testdata
+from tvm.relax.frontend.onnx import from_onnx
+from tvm.testing import env
+
+
+def _build_and_run_network(remote_obj, tracker, mod, input_data):
+    """Helper function to build and run a network."""
+
+    def execute_on_host(mod, inputs):
+        with tvm.transform.PassContext(opt_level=3):
+            ex = tvm.compile(mod, target="llvm")
+        dev = tvm.cpu(0)
+        vm = tvm.relax.VirtualMachine(ex, device=dev)
+        output = vm["main"](*inputs)
+        return output.numpy()
+
+    outputs = []
+    for nnapi in [True, False]:
+        if nnapi:
+            outputs.append(
+                build_and_run(
+                    remote_obj,
+                    tracker,
+                    mod,
+                    input_data,
+                    enable_nnapi=nnapi,
+                )
+            )
+        else:
+            outputs.append(execute_on_host(mod, input_data))
+    return outputs
+
+
+def get_network(name, dtype, input_shape=(1, 3, 224, 224)):
+    def download_model(model_url, name):
+        model_path = download_testdata(model_url, name + ".onnx", module="onnx")
+        onnx_model = onnx.load(model_path)
+
+        shape_dict = {"x": input_shape}
+        mod = from_onnx(onnx_model, shape_dict)
+        return mod
+
+    def create_model(name):
+        if "vgg11" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/vgg11_Opset18_timm/vgg11_Opset18.onnx"
+        elif "mobilenetv3" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/mobilenetv3_large_100_miil_Opset17_timm/mobilenetv3_large_100_miil_Opset17.onnx"
+        elif "alexnet" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/alexnet_Opset17_torch_hub/alexnet_Opset17.onnx"
+        elif "resnet50" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/resnet50_Opset18_timm/resnet50_Opset18.onnx"
+        elif "resnet34" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/resnet34_Opset18_timm/resnet34_Opset18.onnx"
+        elif "resnet18" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/resnet18_Opset18_timm/resnet18_Opset18.onnx"
+        elif "squeezenet" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/squeezenet1_1_Opset18_torch_hub/squeezenet1_1_Opset18.onnx"
+        elif "vgg16" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/vgg16_Opset18_timm/vgg16_Opset18.onnx"
+        elif "vgg19" == name:
+            model_url = "https://github.com/onnx/models/raw/bec48b6a70e5e9042c0badbaafefe4454e072d08/Computer_Vision/vgg19_Opset18_timm/vgg19_Opset18.onnx"
+        else:
+            assert False, f"Not supported model {name}"
+
+        return download_model(model_url, name)
+
+    mod = create_model(name)
+    return mod, {"data": (input_shape, dtype)}
+
+
+@pytest.mark.parametrize(
+    "name",
+    [
+        "alexnet",
+        "vgg11",
+        "vgg16",
+        "vgg19",
+        "resnet18",
+        "resnet34",
+        "resnet50",
+        "squeezenet",
+        "mobilenetv3",
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "float32",
+    ],
+)
+@pytest.mark.skipif(not env.build_flag_enabled("USE_NNAPI_CODEGEN"), reason="need nnapi")
+def test_network(name, dtype):
+    remote_obj, tracker = remote()
+    print(f"Network evaluating {name} with dtype {dtype}")
+    np.random.seed(0)
+    mod, inputs = get_network(name, dtype)
+    input_data = {}
+
+    for _name, (shape, _dtype) in inputs.items():
+        input_data[_name] = np.random.uniform(-1.0, 1.0, shape).astype(_dtype)
+
+    inputs_tvm: list[tvm.runtime.Tensor] = [tvm.runtime.tensor(v) for k, v in input_data.items()]
+    outputs = _build_and_run_network(remote_obj, tracker, mod, inputs_tvm)
+    nnapi_out = outputs[0]
+    expected_out = outputs[1]
+    tvm.testing.assert_allclose(nnapi_out, expected_out, rtol=1e-4, atol=1e-5)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()