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Summary: Adding autoquantization functionality, using hte do_quant api we can test kernel speeds and pick the best quantization type (or no quantization) for each layer. Test Plan: python test/test.py -k "autoquant" also tested on SAM and SDXL pytorch-labs/segment-anything-fast#114 HDCharles/sdxl-fast@8d9942a Reviewers: Subscribers: Tasks: Tags: ghstack-source-id: fddbaf2c203a1745e8a84980f778c45162576cbc Pull Request resolved: #38
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Original file line number | Diff line number | Diff line change |
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from torchao.quantization import ( | ||
apply_weight_only_int8_quant, | ||
apply_dynamic_quant, | ||
change_linear_weights_to_int8_dqtensors, | ||
change_linear_weights_to_int8_woqtensors, | ||
change_linear_weights_to_int4_woqtensors, | ||
swap_conv2d_1x1_to_linear, | ||
autoquant, | ||
change_linears_to_autoquantizable, | ||
change_autoquantizable_to_quantized, | ||
) | ||
|
||
__all__ = [ | ||
"apply_weight_only_int8_quant", | ||
"apply_dynamic_quant", | ||
"change_linear_weights_to_int8_dqtensors", | ||
"change_linear_weights_to_int8_woqtensors", | ||
"change_linear_weights_to_int4_woqtensors", | ||
"swap_conv2d_1x1_to_linear" | ||
"safe_int_mm", | ||
"autoquant", | ||
"change_linears_to_autoquantizable", | ||
"change_autoquantizable_to_quantized", | ||
] |
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