mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-02-19 11:56:43 -05:00
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
# Copyright 2020 The Nod Team. All rights reserved.
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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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import argparse
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import os
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def dir_path(path):
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if os.path.isdir(path):
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return path
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else:
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raise argparse.ArgumentTypeError(
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f"readable_dir:{path} is not a valid path")
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def dir_file(path):
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if os.path.isfile(path):
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return path
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else:
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raise argparse.ArgumentTypeError(
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f"readable_file:{path} is not a valid file")
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parser = argparse.ArgumentParser(description='SHARK runner.')
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parser.add_argument(
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"--device",
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type=str,
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default="cpu",
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help="Device on which shark_runner runs. options are cpu, gpu, and vulkan")
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parser.add_argument(
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"--repro_dir",
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help=
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"Directory to which module files will be saved for reproduction or debugging.",
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type=dir_path,
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default="/tmp/")
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parser.add_argument("--save_mlir",
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default=False,
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action="store_true",
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help="Saves input MLIR module to /tmp/ directory.")
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parser.add_argument("--save_vmfb",
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default=False,
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action="store_true",
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help="Saves iree .vmfb module to /tmp/ directory.")
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parser.add_argument(
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"--model_config_path",
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help="Directory to where the tuned model config file is located.",
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default=None)
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parser.add_argument(
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"--num_warmup_iterations",
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type=int,
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default=2,
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help="Run the model for the specified number of warmup iterations.")
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parser.add_argument(
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"--num_iterations",
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type=int,
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default=1,
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help="Run the model for the specified number of iterations.")
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shark_args, unknown = parser.parse_known_args()
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