Enable autoquant for CPU userbenchmark #2547
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zxd1997066
changed the title
chuanqi129
reviewed
Dec 16, 2024
| change_linear_weights_to_int8_dqtensors, | ||
| change_linear_weights_to_int8_woqtensors, | ||
| ) | ||
| if model.device == "cuda": |
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Let's keep original behavior in here, suggest to change as
if model.device == "cpu":
xxx
else:
original path
| if args.quantization == "auto_quant": | ||
| module, example_inputs = model.get_module() | ||
| with torch.no_grad(): | ||
| module=torchao.autoquant(torch.compile(module, mode='max-autotune')) |
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why we need compile the module before autoquant? and why we need to hard code the compile mode as max-autotune? Can we follow similar style with original part?
| elif args.quantization == "int4weightonly": | ||
| change_linear_weights_to_int4_woqtensors(module) | ||
| elif model.device == "cpu" and model.test == "eval": | ||
| if args.quantization == "auto_quant": |
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add else statement to warining that the other quant modes are still not support for cpu device
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