Outline Benchmark Quant APIs #1706
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1706
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New FailureAs of commit 5d03125 with merge base aa51486 ( NEW FAILURE - The following job has failed:
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Feb 13, 2025
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| def parse_quantization_arg(quantization_input: List[str]): | ||
| # Define regex patterns for quantization techniques | ||
| patterns = { |
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Will be adding more patterns here, for all quant apis
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This is the first PR, in the benchmarking effort. It provides the outline to building microbenchmarking framework for quant api's in torchao.
The different inputs like quantization techniques, matrix sizes, compile, sparsity etc, will be given as input to the python script. The options are re-defined in the scipt for quantization techniques, and a developer can add new quant technique. The script will generate a csv with performance numbers, and that'll be used to plot charts and as an input to dashboard
Included in current PR:
Example: