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Enable autoquant for CPU userbenchmark #2547

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Enable autoquant for CPU userbenchmark #2547

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2e3c4d0
enable autoquant for cpu
zxd1997066 Nov 25, 2024
dde5b66
refine the code
zxd1997066 Dec 22, 2024
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49 changes: 30 additions & 19 deletions torchbenchmark/util/backends/torchdynamo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ def parse_torchdynamo_args(dynamo_args: List[str]) -> argparse.Namespace:
)
parser.add_argument(
"--quantization",
choices=["int8dynamic", "int8weightonly", "int4weightonly"],
choices=["int8dynamic", "int8weightonly", "int4weightonly", "auto_quant"],
help="Apply quantization to the model before running it",
)
parser.add_argument(
Expand Down Expand Up @@ -183,25 +183,36 @@ def apply_torchdynamo_args(

if args.quantization:
import torchao
from torchao.quantization import (
change_linear_weights_to_int4_woqtensors,
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

from torchao.quantization import (
change_linear_weights_to_int4_woqtensors,
change_linear_weights_to_int8_dqtensors,
change_linear_weights_to_int8_woqtensors,
)

torch._dynamo.config.automatic_dynamic_shapes = False
torch._dynamo.config.force_parameter_static_shapes = False
torch._dynamo.config.cache_size_limit = 1000
assert "cuda" in model.device
module, example_inputs = model.get_module()
if args.quantization == "int8dynamic":
torch._inductor.config.force_fuse_int_mm_with_mul = True
change_linear_weights_to_int8_dqtensors(module)
elif args.quantization == "int8weightonly":
torch._inductor.config.use_mixed_mm = True
change_linear_weights_to_int8_woqtensors(module)
elif args.quantization == "int4weightonly":
change_linear_weights_to_int4_woqtensors(module)
torch._dynamo.config.automatic_dynamic_shapes = False
torch._dynamo.config.force_parameter_static_shapes = False
torch._dynamo.config.cache_size_limit = 1000
assert "cuda" in model.device
module, example_inputs = model.get_module()
if args.quantization == "int8dynamic":
torch._inductor.config.force_fuse_int_mm_with_mul = True
change_linear_weights_to_int8_dqtensors(module)
elif args.quantization == "int8weightonly":
torch._inductor.config.use_mixed_mm = True
change_linear_weights_to_int8_woqtensors(module)
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

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?

if isinstance(example_inputs, dict):
module(**example_inputs)
else:
module(*example_inputs)
model.set_module(module)

if args.freeze_prepack_weights:
torch._inductor.config.freezing = True
Expand Down