chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
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+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed 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 argparse
+import onnx_graphsurgeon as gs
+import numpy as np
+import onnx
+import ctypes
+
+import tensorrt as trt
+from polygraphy.backend.trt import (
+    CreateConfig,
+    EngineFromNetwork,
+    NetworkFromOnnxPath,
+    TrtRunner,
+)
+
+
+def parseArgs():
+    parser = argparse.ArgumentParser(
+        description="Options for Circular Padding plugin C++ example"
+    )
+
+    parser.add_argument(
+        "--precision",
+        type=str,
+        default="fp32",
+        choices=["fp32", "fp16"],
+        help="Precision to use for plugin",
+    )
+    parser.add_argument(
+        "--plugin-lib",
+        type=str,
+        help="Path to the Circular Padding plugin lib",
+        required=True,
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+
+    args = parseArgs()
+
+    handle = ctypes.CDLL(args.plugin_lib)
+    if not handle:
+        raise RuntimeError("Could not load Circular Padding plugin library")
+
+    precision = np.float32 if args.precision == "fp32" else np.float16
+    inp_shape = (10, 3, 32, 32)
+    X = np.random.normal(size=inp_shape).astype(precision)
+
+    pads = (1, 1, 1, 1)
+
+    # create ONNX model
+    onnx_path = f"test_CircPadPlugin_cpp_{args.precision}.onnx"
+    inputA = gs.Variable(name="X", shape=inp_shape, dtype=precision)
+    Y = gs.Variable(name="Y", dtype=precision)
+    myPluginNode = gs.Node(
+        name="CircPadPlugin",
+        op="CircPadPlugin",
+        inputs=[inputA],
+        outputs=[Y],
+        attrs={"pads": pads},
+    )
+    graph = gs.Graph(nodes=[myPluginNode], inputs=[inputA], outputs=[Y], opset=16)
+    onnx.save(gs.export_onnx(graph), onnx_path)
+
+    # build engine
+    build_engine = EngineFromNetwork(
+        NetworkFromOnnxPath(onnx_path, strongly_typed=True), CreateConfig()
+    )
+
+    Y_ref = np.pad(X, [[0, 0], [0, 0], [pads[0], pads[1]], [pads[2], pads[3]]], "wrap")
+    # Run
+    with TrtRunner(build_engine, "trt_runner") as runner:
+        outputs = runner.infer({"X": X})
+        Y = outputs["Y"]
+
+        if np.allclose(Y, Y_ref):
+            print("Inference result correct!")
+        else:
+            print("Inference result incorrect!")