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4972cf06a2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41145 **Summary** This commit adds out-of-source-tree tests for `to_backend`. These tests check that a Module can be lowered to a backend, exported, loaded (in both Python and C++) and executed. **Fixes** This commit fixes #40067. Test Plan: Imported from OSS Reviewed By: jamesr66a Differential Revision: D22510076 Pulled By: SplitInfinity fbshipit-source-id: f65964ef3092a095740f06636ed5b1eb0884492d
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import argparse
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import os.path
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import sys
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import torch
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def get_custom_backend_library_path():
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"""
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Get the path to the library containing the custom backend.
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Return:
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The path to the custom backend object, customized by platform.
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"""
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if sys.platform.startswith("win32"):
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library_filename = "custom_backend.dll"
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elif sys.platform.startswith("darwin"):
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library_filename = "libcustom_backend.dylib"
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else:
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library_filename = "libcustom_backend.so"
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path = os.path.abspath("build/{}".format(library_filename))
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assert os.path.exists(path), path
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return path
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def to_custom_backend(module):
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"""
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This is a helper that wraps torch._C._jit_to_test_backend and compiles
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only the forward method with an empty compile spec.
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Args:
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module: input ScriptModule.
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Returns:
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The module, lowered so that it can run on TestBackend.
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"""
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lowered_module = torch._C._jit_to_backend("custom_backend", module._c, {"forward": {"": ""}})
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return lowered_module
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class Model(torch.nn.Module):
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"""
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Simple model used for testing that to_backend API supports saving, loading,
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and executing in C++.
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"""
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def __init__(self):
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super(Model, self).__init__()
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def forward(self, a, b):
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return (a + b, a - b)
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def main():
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parser = argparse.ArgumentParser(
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description="Lower a Module to a custom backend"
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)
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parser.add_argument("--export-module-to", required=True)
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options = parser.parse_args()
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# Load the library containing the custom backend.
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library_path = get_custom_backend_library_path()
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torch.ops.load_library(library_path)
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assert library_path in torch.ops.loaded_libraries
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# Lower an instance of Model to the custom backend and export it
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# to the specified location.
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lowered_module = to_custom_backend(torch.jit.script(Model()))
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torch.jit.save(lowered_module, options.export_module_to)
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if __name__ == "__main__":
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main()
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