wehub-resource-sync
82 lines
2.8 KiB
Python
82 lines
2.8 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 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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torch = mod.lazy_import("torch>=1.13.0")
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@mod.export()
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class PytRunner(BaseRunner):
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"""
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Runs inference using PyTorch.
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"""
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def __init__(self, model, input_metadata, output_names, name=None):
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"""
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Args:
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model (Union[torch.nn.Module, Callable() -> torch.nn.Module]):
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A torch.nn.Module or subclass or a callable that returns one.
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input_metadata (TensorMetadata): Mapping of input names to their data types and shapes.
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output_names (List[str]):
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A list of output names of the model. This information is used by the
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Comparator to determine which outputs to compare.
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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="pytorch-runner")
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self._model = model
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self.input_metadata = input_metadata
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self.output_names = output_names
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@util.check_called_by("activate")
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def activate_impl(self):
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self.model, _ = util.invoke_if_callable(self._model)
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self.model.eval()
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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 self.input_metadata
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@util.check_called_by("infer")
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def infer_impl(self, feed_dict):
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with torch.no_grad():
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inputs = [
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torch.from_numpy(val.astype(dtype)).cuda()
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for (val, (dtype, _)) in zip(
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feed_dict.values(), self.input_metadata.values()
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)
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]
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start = time.time()
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outputs = self.model(*inputs)
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end = time.time()
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out_dict = OrderedDict()
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for name, output in zip(self.output_names, outputs):
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out_dict[name] = output.cpu().numpy()
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return out_dict, end - start
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@util.check_called_by("deactivate")
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def deactivate_impl(self):
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del self.model
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