6520b21ce0
This patch adds initial support for Integrated Distributed ThinLTO (DTLTO) in LLVM, which manages distribution internally during the traditional link step. This enables compatibility with any build system that supports in-process ThinLTO. In contrast, existing approaches to distributed ThinLTO, which split the thin-link (--thinlto-index-only), backend compilation, and final link into separate steps, require build system support, e.g. Bazel. This patch implements the core DTLTO mechanism, which enables delegation of ThinLTO backend jobs to an external process (the distributor). The distributor can then manage job distribution through systems like Incredibuild. A generic JSON interface is used to communicate with the distributor, allowing for the creation of new distributors (and thus integration with different distribution systems) without modifying LLVM. Please see llvm/docs/dtlto.rst for more details. RFC: https://discourse.llvm.org/t/rfc-integrated-distributed-thinlto/69641 Design Review: https://github.com/llvm/llvm-project/pull/126654
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
"""
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DTLTO Mock Distributor.
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This script acts as a mock distributor for Distributed ThinLTO (DTLTO). It is
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used for testing DTLTO when a Clang binary is not be available to invoke to
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perform the backend compilation jobs.
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Usage:
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python mock.py <input_file1> <input_file2> ... <json_file>
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Arguments:
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- <input_file1>, <input_file2>, ... : Input files to be copied.
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- <json_file> : JSON file describing the DTLTO jobs.
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The script performs the following:
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1. Reads the JSON file containing job descriptions.
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2. For each job copies the corresponding input file to the output location
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specified for that job.
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3. Validates the JSON format using the `validate` module.
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"""
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import sys
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import json
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import shutil
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from pathlib import Path
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import validate
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if __name__ == "__main__":
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json_arg = sys.argv[-1]
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input_files = sys.argv[1:-1]
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# Load the DTLTO information from the input JSON file.
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with Path(json_arg).open() as f:
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data = json.load(f)
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# Iterate over the jobs and create the output
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# files by copying over the supplied input files.
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for job_index, job in enumerate(data["jobs"]):
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shutil.copy(input_files[job_index], job["outputs"][0])
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# Check the format of the JSON.
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validate.validate(data)
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