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
@@ -0,0 +1,83 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2024 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 copy
+import time
+from collections import OrderedDict
+
+from polygraphy import mod, util
+from polygraphy.backend.base import BaseRunner
+from polygraphy.backend.pluginref.references import OP_REGISTRY
+from polygraphy.logger import G_LOGGER
+
+np = mod.lazy_import("numpy")
+onnx_util = mod.lazy_import("polygraphy.backend.onnx.util")
+
+
+@mod.export()
+class PluginRefRunner(BaseRunner):
+    """
+    Runs inference using custom CPU reference implementations
+    """
+
+    def __init__(self, graph, name=None):
+        """
+        Args:
+            graph (Union[onnx_graphsurgeon.Graph, Callable() -> onnx_graphsurgeon.Graph]):
+                    An ONNX-GraphSurgeon graph or a callable that returns one.
+            name (str):
+                    The human-readable name prefix to use for this runner.
+                    A runner count and timestamp will be appended to this prefix.
+        """
+        super().__init__(name=name, prefix="pluginref-runner")
+        self._graph = graph
+
+    @util.check_called_by("activate")
+    def activate_impl(self):
+        self.graph, _ = util.invoke_if_callable(self._graph)
+
+    @util.check_called_by("get_input_metadata")
+    def get_input_metadata_impl(self):
+        return onnx_util.meta_from_gs_tensors(self.graph.inputs)
+
+    @util.check_called_by("infer")
+    def infer_impl(self, feed_dict):
+        start = time.time()
+
+        intermediate_tensors = copy.copy(feed_dict)
+        for node in self.graph.nodes:
+            if node.op not in OP_REGISTRY:
+                G_LOGGER.critical(
+                    f"Op: {node.op} does not have a reference implementation registered!"
+                )
+
+            intermediate_tensors.update(
+                OP_REGISTRY[node.op](node, intermediate_tensors)
+            )
+
+        outputs = OrderedDict()
+        for out in self.graph.outputs:
+            outputs[out.name] = intermediate_tensors[out.name]
+
+        end = time.time()
+
+        self.inference_time = end - start
+        return outputs
+
+    @util.check_called_by("deactivate")
+    def deactivate_impl(self):
+        del self.graph