wehub-resource-sync
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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import time
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from collections import OrderedDict
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from polygraphy import mod, util
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from polygraphy.backend.base import BaseRunner
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from polygraphy.backend.pluginref.references import OP_REGISTRY
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from polygraphy.logger import G_LOGGER
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np = mod.lazy_import("numpy")
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onnx_util = mod.lazy_import("polygraphy.backend.onnx.util")
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@mod.export()
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class PluginRefRunner(BaseRunner):
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"""
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Runs inference using custom CPU reference implementations
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"""
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def __init__(self, graph, name=None):
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"""
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Args:
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graph (Union[onnx_graphsurgeon.Graph, Callable() -> onnx_graphsurgeon.Graph]):
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An ONNX-GraphSurgeon graph or a callable that returns one.
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name (str):
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The human-readable name prefix to use for this runner.
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A runner count and timestamp will be appended to this prefix.
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"""
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super().__init__(name=name, prefix="pluginref-runner")
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self._graph = graph
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@util.check_called_by("activate")
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def activate_impl(self):
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self.graph, _ = util.invoke_if_callable(self._graph)
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@util.check_called_by("get_input_metadata")
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def get_input_metadata_impl(self):
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return onnx_util.meta_from_gs_tensors(self.graph.inputs)
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@util.check_called_by("infer")
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def infer_impl(self, feed_dict):
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start = time.time()
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intermediate_tensors = copy.copy(feed_dict)
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for node in self.graph.nodes:
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if node.op not in OP_REGISTRY:
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G_LOGGER.critical(
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f"Op: {node.op} does not have a reference implementation registered!"
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)
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intermediate_tensors.update(
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OP_REGISTRY[node.op](node, intermediate_tensors)
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)
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outputs = OrderedDict()
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for out in self.graph.outputs:
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outputs[out.name] = intermediate_tensors[out.name]
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end = time.time()
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self.inference_time = end - start
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return outputs
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@util.check_called_by("deactivate")
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def deactivate_impl(self):
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del self.graph
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