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replace FbgemmConfig with Int4WeightOnlyConfig #2727

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replace FbgemmConfig with Int4WeightOnlyConfig #2727

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28 changes: 5 additions & 23 deletions torchao/prototype/awq/example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
from torchao.quantization import (
quantize_,
)
from torchao.quantization.quant_api import Int4WeightOnlyConfig


# adapted from: https://github.com/mit-han-lab/llm-awq/blob/main/awq/entry.py#L255
Expand Down Expand Up @@ -223,18 +224,7 @@ def quantize_and_eval(
if quant.startswith("awq-int4wo"):
group_size = int(quant.split("-")[2])
print(f"running {quant} quantization with group size {group_size}")
# TODO: this is temporary, we'll be using Int4WeightOnlyConfig soon
from torchao.quantization import FbgemmConfig

# use_hqq = True
# base_config = Int4WeightOnlyConfig(group_size=group_size, use_hqq=use_hqq)
base_config = FbgemmConfig(
input_dtype=torch.bfloat16,
weight_dtype=torch.int4,
output_dtype=torch.bfloat16,
block_size=[1, group_size],
preshuffle=False,
)
base_config = Int4WeightOnlyConfig(group_size=group_size, use_hqq=True)
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need to remove use_hqq and add version=2 for now, we still want to use the fbgemm kernel

base_config = Int4WeightOnlyConfig(group_size=group_size, version=2)

print(f"running {quant} prepare and calibrate")
t0 = time.time()
quant_config = AWQConfig(base_config, step="prepare")
Expand Down Expand Up @@ -267,17 +257,9 @@ def quantize_and_eval(
elif quant.startswith("int4wo"):
group_size = int(quant.split("-")[1])
print(f"running {quant} quantization with group size {group_size}")
# TODO: enable after refactor: https://github.com/pytorch/ao/pull/2474
# use_hqq = "hqq" in quant
# base_config = Int4WeightOnlyConfig(group_size=group_size, use_hqq=use_hqq)
int4_weight_only_config = FbgemmConfig(
input_dtype=torch.bfloat16,
weight_dtype=torch.int4,
output_dtype=torch.bfloat16,
block_size=[1, group_size],
preshuffle=False,
)
quantize_(model, int4_weight_only_config)
use_hqq = "hqq" in quant
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same here

base_config = Int4WeightOnlyConfig(group_size=group_size, use_hqq=use_hqq)
quantize_(model, base_config)

if model_save_path is not None:
print(f"Saving model to {model_save_path}")
Expand Down